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산업경영공학과)","userId":"14719868373122672648"}},"outputId":"3c3c61fb-c70b-43c1-ce74-8c824f3cc3ac"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.46.2)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.16.1)\n","Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.26.2)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.2)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.2)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.9.11)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n","Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.5)\n","Requirement already satisfied: tokenizers<0.21,>=0.20 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.20.3)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.6)\n","Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (2024.10.0)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (4.12.2)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4.0)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.10)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.2.3)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.8.30)\n"]}],"source":["!pip install transformers"]},{"cell_type":"code","source":["!pip install adabelief-pytorch==0.2.0"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H7QdM7g1zrUQ","executionInfo":{"status":"ok","timestamp":1731900315039,"user_tz":-540,"elapsed":2927,"user":{"displayName":"‍임주현[학생](공과대학 산업경영공학과)","userId":"14719868373122672648"}},"outputId":"82643ff4-223b-4498-99f4-0b8ac330448b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting adabelief-pytorch==0.2.0\n","  Downloading adabelief_pytorch-0.2.0-py3-none-any.whl.metadata (616 bytes)\n","Requirement already satisfied: torch>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from adabelief-pytorch==0.2.0) (2.5.1+cu121)\n","Collecting colorama>=0.4.0 (from adabelief-pytorch==0.2.0)\n","  Downloading colorama-0.4.6-py2.py3-none-any.whl.metadata (17 kB)\n","Requirement already satisfied: tabulate>=0.7 in /usr/local/lib/python3.10/dist-packages (from adabelief-pytorch==0.2.0) (0.9.0)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=0.4.0->adabelief-pytorch==0.2.0) (3.16.1)\n","Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=0.4.0->adabelief-pytorch==0.2.0) (4.12.2)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=0.4.0->adabelief-pytorch==0.2.0) (3.4.2)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=0.4.0->adabelief-pytorch==0.2.0) (3.1.4)\n","Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=0.4.0->adabelief-pytorch==0.2.0) (2024.10.0)\n","Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch>=0.4.0->adabelief-pytorch==0.2.0) (1.13.1)\n","Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch>=0.4.0->adabelief-pytorch==0.2.0) (1.3.0)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=0.4.0->adabelief-pytorch==0.2.0) (3.0.2)\n","Downloading adabelief_pytorch-0.2.0-py3-none-any.whl (5.7 kB)\n","Downloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n","Installing collected packages: colorama, adabelief-pytorch\n","Successfully installed adabelief-pytorch-0.2.0 colorama-0.4.6\n"]}]},{"cell_type":"code","source":["!pip install -U nbformat # 기설치시, update"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"p2NX-jFqztOR","executionInfo":{"status":"ok","timestamp":1731900317668,"user_tz":-540,"elapsed":2632,"user":{"displayName":"‍임주현[학생](공과대학 산업경영공학과)","userId":"14719868373122672648"}},"outputId":"c73b686f-86e8-4460-a411-5f3fbf9a693a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: nbformat in /usr/local/lib/python3.10/dist-packages (5.10.4)\n","Requirement already satisfied: fastjsonschema>=2.15 in /usr/local/lib/python3.10/dist-packages (from nbformat) (2.20.0)\n","Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.10/dist-packages (from nbformat) (4.23.0)\n","Requirement already satisfied: jupyter-core!=5.0.*,>=4.12 in /usr/local/lib/python3.10/dist-packages (from nbformat) (5.7.2)\n","Requirement already satisfied: traitlets>=5.1 in /usr/local/lib/python3.10/dist-packages (from nbformat) (5.7.1)\n","Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat) (24.2.0)\n","Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat) (2024.10.1)\n","Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat) (0.35.1)\n","Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat) (0.21.0)\n","Requirement already satisfied: platformdirs>=2.5 in /usr/local/lib/python3.10/dist-packages (from jupyter-core!=5.0.*,>=4.12->nbformat) (4.3.6)\n"]}]},{"cell_type":"code","source":["import os\n","import re\n","import numpy as np\n","from tqdm import tqdm\n","from glob import glob\n","import json\n","import requests\n","import tensorflow as tf\n","from transformers import BertModel, TFBertModel, TFRobertaModel, RobertaTokenizer\n","from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModel, AutoModelForSequenceClassification\n","from tensorflow.keras.preprocessing.sequence import pad_sequences\n","from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","from sklearn.model_selection import train_test_split,KFold,StratifiedKFold\n","from adabelief_pytorch import AdaBelief\n","from transformers.optimization import get_cosine_schedule_with_warmup\n","from tqdm import tqdm, tqdm_notebook\n","import shutil\n","import gc\n","import torch\n","import torch.nn as nn\n","import torch.optim as optim\n","import torch.nn.functional as F\n","from torch.utils.data import Dataset, DataLoader\n","import urllib.request\n","\n","from sklearn.metrics import precision_score, recall_score, f1_score, classification_report"],"metadata":{"id":"JOpRDpYCzu8a"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import os\n","from google.colab import drive\n","drive.mount('/content/drive/')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Cjhd8qn8zyqv","executionInfo":{"status":"ok","timestamp":1731900341458,"user_tz":-540,"elapsed":13064,"user":{"displayName":"‍임주현[학생](공과대학 산업경영공학과)","userId":"14719868373122672648"}},"outputId":"91e28a29-c2e6-4ed7-c995-5e3244e3b823"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount(\"/content/drive/\", force_remount=True).\n"]}]},{"cell_type":"code","source":["# 초기에 소스 파일이 위치하는 Colab 노트북 기본 파일 디렉토리로 이동\n","%cd /content/drive/MyDrive/KHUDA 금융/심화트랙"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YfCjtYEKz2ln","executionInfo":{"status":"ok","timestamp":1731900341458,"user_tz":-540,"elapsed":3,"user":{"displayName":"‍임주현[학생](공과대학 산업경영공학과)","userId":"14719868373122672648"}},"outputId":"cbd37c59-8ae7-4e6c-be78-818c83421fba"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/KHUDA 금융/심화트랙\n"]}]},{"cell_type":"code","source":["#random seed 고정\n","tf.random.set_seed(1234)\n","np.random.seed(1234)\n","BATCH_SIZE = 32\n","NUM_EPOCHS = 3\n","\n","L_RATE = 1e-5\n","MAX_LEN = 128\n","max_grad_norm=1\n","log_interval=200\n","NUM_CORES = os.cpu_count()\n","device = torch.device(\"cuda:0\")\n","\n","DATA_IN_PATH = './데이터'\n","DATA_OUT_PATH = \"./데이터\""],"metadata":{"id":"gTF1vZGSz07C"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["DATASET_URL = \"https://raw.githubusercontent.com/ukairia777/finance_sentiment_corpus/main/finance_data.csv\"\n","DATASET_NAME = \"finance_data.csv\""],"metadata":{"id":"YtusUhWT0Nv5"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["urllib.request.urlretrieve(DATASET_URL,\n","                           filename = DATASET_NAME\n","                           )"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sMfdYggc0PiC","executionInfo":{"status":"ok","timestamp":1731900355524,"user_tz":-540,"elapsed":1357,"user":{"displayName":"‍임주현[학생](공과대학 산업경영공학과)","userId":"14719868373122672648"}},"outputId":"7d6bf45a-5f9e-4471-ea3a-c17a386a4b6a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["('finance_data.csv', <http.client.HTTPMessage at 0x794be59a7670>)"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["dataset = pd.read_csv(DATASET_NAME)\n","dataset.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":289},"id":"5z_5NqzW0Q2Q","executionInfo":{"status":"ok","timestamp":1731900358017,"user_tz":-540,"elapsed":424,"user":{"displayName":"‍임주현[학생](공과대학 산업경영공학과)","userId":"14719868373122672648"}},"outputId":"1fd65a57-83f9-46a1-97c2-9e4b1be2dc01"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     labels                                           sentence  \\\n","0   neutral  According to Gran, the company has no plans to...   \n","1   neutral  Technopolis plans to develop in stages an area...   \n","2  negative  The international electronic industry company ...   \n","3  positive  With the new production plant the company woul...   \n","4  positive  According to the company's updated strategy fo...   \n","\n","                                        kor_sentence  \n","0  Gran에 따르면, 그 회사는 회사가 성장하고 있는 곳이지만, 모든 생산을 러시아로...  \n","1  테크노폴리스는 컴퓨터 기술과 통신 분야에서 일하는 회사들을 유치하기 위해 10만 평...  \n","2  국제 전자산업 회사인 엘코텍은 탈린 공장에서 수십 명의 직원을 해고했으며, 이전의 ...  \n","3  새로운 생산공장으로 인해 회사는 예상되는 수요 증가를 충족시킬 수 있는 능력을 증가...  \n","4  2009-2012년 회사의 업데이트된 전략에 따르면, Basware는 20% - 4...  "],"text/html":["\n","  <div id=\"df-3f0d7d08-952e-439d-9dc9-5bf2b502a1e3\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>labels</th>\n","      <th>sentence</th>\n","      <th>kor_sentence</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>neutral</td>\n","      <td>According to Gran, the company has no plans to...</td>\n","      <td>Gran에 따르면, 그 회사는 회사가 성장하고 있는 곳이지만, 모든 생산을 러시아로...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>neutral</td>\n","      <td>Technopolis plans to develop in stages an area...</td>\n","      <td>테크노폴리스는 컴퓨터 기술과 통신 분야에서 일하는 회사들을 유치하기 위해 10만 평...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>negative</td>\n","      <td>The international electronic industry company ...</td>\n","      <td>국제 전자산업 회사인 엘코텍은 탈린 공장에서 수십 명의 직원을 해고했으며, 이전의 ...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>positive</td>\n","      <td>With the new production plant the company woul...</td>\n","      <td>새로운 생산공장으로 인해 회사는 예상되는 수요 증가를 충족시킬 수 있는 능력을 증가...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>positive</td>\n","      <td>According to the company's updated strategy fo...</td>\n","      <td>2009-2012년 회사의 업데이트된 전략에 따르면, Basware는 20% - 4...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3f0d7d08-952e-439d-9dc9-5bf2b502a1e3')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-3f0d7d08-952e-439d-9dc9-5bf2b502a1e3 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n","  dataset['labels'] = dataset['labels'].replace(['neutral', 'positive', 'negative'],[0, 1, 2])\n"]}]},{"cell_type":"code","source":["#두가지 임베딩\n","#\"klue/roberta-large\"\n","#\"monologg/kobigbird-bert-base\"\n","#tokenizer = AutoTokenizer.from_pretrained(\"monologg/kobigbird-bert-base\", cache_dir='bert_ckpt', do_lower_case=False)\n","tokenizer = AutoTokenizer.from_pretrained(\"klue/roberta-base\", cache_dir='bert_ckpt', do_lower_case=False)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Kl3gaNZn0YnB","executionInfo":{"status":"ok","timestamp":1731900372086,"user_tz":-540,"elapsed":6345,"user":{"displayName":"‍임주현[학생](공과대학 산업경영공학과)","userId":"14719868373122672648"}},"outputId":"0f53cdb0-9993-4f82-864f-0fd0817b8a8d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n"]}]},{"cell_type":"code","source":["dataset = pd.read_csv('./데이터/dataset_prep.csv')\n","dataset.drop('Unnamed: 0',axis=1,inplace=True)"],"metadata":{"id":"g1ONhtX20dqm"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 입출력 데이터 분리\n","X_data = dataset['kor_sentence']\n","y_data = dataset['labels']"],"metadata":{"id":"MX_Bl-hq0fNG"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["TEST_SIZE = 0.2 # Train: Test = 8 :2 분리\n","RANDOM_STATE = 42\n","# strtify = True 일 경우, 데이터 분리 이전의 라벨별 분포 고려\n","X_train, X_test, y_train, y_test = train_test_split(X_data, y_data,\n","                                                    test_size = TEST_SIZE,\n","                                                    random_state = RANDOM_STATE,\n","                                                    stratify = y_data)"],"metadata":{"id":"0Hth-_yF0j2N"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(f\"훈련 입력 데이터 개수: {len(X_train)}\")\n","print(f\"테스트 입력 데이터 개수: {len(X_test)}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"k9K3ZJZN0icH","executionInfo":{"status":"ok","timestamp":1731900422384,"user_tz":-540,"elapsed":454,"user":{"displayName":"‍임주현[학생](공과대학 산업경영공학과)","userId":"14719868373122672648"}},"outputId":"6b0443ee-66a3-4379-a9ed-f61ab7049b42"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["훈련 입력 데이터 개수: 3861\n","테스트 입력 데이터 개수: 966\n"]}]},{"cell_type":"code","source":["# 훈련 데이터 라벨별 비율\n","y_train.value_counts(normalize = True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":207},"id":"U3C-ry8o0loq","executionInfo":{"status":"ok","timestamp":1731900425562,"user_tz":-540,"elapsed":578,"user":{"displayName":"‍임주현[학생](공과대학 산업경영공학과)","userId":"14719868373122672648"}},"outputId":"c66b0dfe-91b7-4491-f1c7-e2fab298e5e4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["labels\n","0    0.592852\n","1    0.282051\n","2    0.125097\n","Name: proportion, dtype: float64"],"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>proportion</th>\n","    </tr>\n","    <tr>\n","      <th>labels</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.592852</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.282051</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.125097</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div><br><label><b>dtype:</b> float64</label>"]},"metadata":{},"execution_count":18}]},{"cell_type":"code","source":["# 테스트 데이터 라벨별 비율\n","y_test.value_counts(normalize = True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":207},"id":"bU4ohfdS0p71","executionInfo":{"status":"ok","timestamp":1731900425934,"user_tz":-540,"elapsed":2,"user":{"displayName":"‍임주현[학생](공과대학 산업경영공학과)","userId":"14719868373122672648"}},"outputId":"f7bee0f3-416f-4a1c-dc3c-be5f2c0a15d8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["labels\n","0    0.592133\n","1    0.282609\n","2    0.125259\n","Name: proportion, dtype: float64"],"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>proportion</th>\n","    </tr>\n","    <tr>\n","      <th>labels</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.592133</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.282609</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.125259</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div><br><label><b>dtype:</b> float64</label>"]},"metadata":{},"execution_count":19}]},{"cell_type":"code","source":["# 데이터셋으로 변환해주기 위해 형식을 맞추도록 하겠습니다.\n","X=pd.concat([X_train,y_train],axis=1)\n","X.reset_index(drop=True,inplace=True)\n","X_test=X_test.reset_index()"],"metadata":{"id":"a3oYT6zM0raN"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class TrainDataset(Dataset):\n","    def __init__(self, df):\n","        self.df_data = df\n","    def __getitem__(self, index):\n","        # get the sentence from the dataframe\n","        sentence = self.df_data.loc[index, 'kor_sentence']\n","        encoded_dict = tokenizer(\n","          text = sentence,\n","          add_special_tokens = True,\n","          max_length = MAX_LEN,\n","          pad_to_max_length = True,\n","          truncation=True,           # Pad & truncate all sentences.\n","          return_tensors=\"pt\")\n","\n","        padded_token_list = encoded_dict['input_ids'][0]\n","        token_type_id = encoded_dict['token_type_ids'][0]\n","        att_mask = encoded_dict['attention_mask'][0]\n","        target = torch.tensor(self.df_data.loc[index, \"labels\"])\n","        sample = (padded_token_list, token_type_id , att_mask, target)\n","        return sample\n","    def __len__(self):\n","        return len(self.df_data)"],"metadata":{"id":"h86S_MRi0ujq"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class TestDataset(Dataset):\n","    def __init__(self, df):\n","        self.df_data = df\n","    def __getitem__(self, index):\n","        # get the sentence from the dataframe\n","        sentence = self.df_data.loc[index, 'kor_sentence']\n","        encoded_dict = tokenizer(\n","          text = sentence,\n","          add_special_tokens = True,\n","          max_length = MAX_LEN,\n","          pad_to_max_length = True,\n","          truncation=True,           # Pad & truncate all sentences.\n","          return_tensors=\"pt\")\n","\n","        padded_token_list = encoded_dict['input_ids'][0]\n","        token_type_id = encoded_dict['token_type_ids'][0]\n","        att_mask = encoded_dict['attention_mask'][0]\n","        sample = (padded_token_list, token_type_id , att_mask)\n","        return sample\n","    def __len__(self):\n","        return len(self.df_data)"],"metadata":{"id":"F0qIWJBP0w2b"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def calc_accuracy(X,Y):\n","    max_vals, max_indices = torch.max(X, 1)\n","    train_acc = (max_indices == Y).sum().data.cpu().numpy()/max_indices.size()[0]\n","    return train_acc"],"metadata":{"id":"oXJe0RRZ0yhH"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#AUto\n","train_data = TrainDataset(X)\n","\n","test_data = TestDataset(X_test)\n","\n","train_dataloader = torch.utils.data.DataLoader(train_data,\n","                                        batch_size=BATCH_SIZE,\n","                                        shuffle=True,\n","                                      num_workers=0)\n","test_dataloader = torch.utils.data.DataLoader(test_data,\n","                                        batch_size=BATCH_SIZE,\n","                                        shuffle=False,\n","                                      num_workers=0)\n","#두가지 모델\n","#\"klue/roberta-base\"\n","#kobigbird-bert-base\n","model = AutoModelForSequenceClassification.from_pretrained(\"klue/roberta-base\",num_labels=3)\n","\n","\n","\n","####미세조정\n","n=0\n","for name, child in model.named_children():\n","    if n==0:\n","      h=0\n","      for param in child.parameters():\n","        if h<=0: #이부분 숫자 조절로 fine-tuning => Roberta229: h=229\n","          param.requires_grad = False\n","        h+=1\n","    n+=1\n","#####\n","    # print(param)\n","model.to(device)\n","optimizer = AdaBelief(model.parameters(), lr=1e-5, eps=1e-16, betas=(0.9,0.999), weight_decouple = True, rectify = False)\n","\n","warmup_ratio = 0.1\n","t_total = len(train_dataloader) * NUM_EPOCHS\n","warmup_step = int(t_total * warmup_ratio)\n","scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=warmup_step, num_training_steps=t_total)\n","\n","for e in range(NUM_EPOCHS):\n","    train_acc = 0.0\n","    test_acc = 0.0\n","    best_acc =0.0\n","    model.train()\n","    torch.set_grad_enabled(True)\n","    for batch_id, (input_id,token_type_id,attention_mask,label) in enumerate(tqdm_notebook(train_dataloader)):\n","        optimizer.zero_grad()\n","        input_id = input_id.long().to(device)\n","        token_type_id = token_type_id.long().to(device)\n","        attention_mask = attention_mask.long().to(device)\n","        label = label.to(device)\n","        outputs = model(input_ids=input_id, token_type_ids=token_type_id, attention_mask=attention_mask, labels=label)\n","        loss = outputs[0]\n","        out = outputs[1]\n","        loss.backward()\n","        torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm)\n","        optimizer.step()\n","        scheduler.step()  # Update learning rate schedule\n","        _, preds = torch.max(out, dim=1)\n","        train_acc += f1_score(preds.cpu(), label.cpu(),average='weighted')\n","        if batch_id % log_interval == 0:\n","            print(\"epoch {} batch id {} loss {} train acc {}\".format(e+1, batch_id+1, loss.data.cpu().numpy(), train_acc / (batch_id+1)))\n","    print(\"epoch {} train acc {}\".format(e+1, train_acc / (batch_id+1)))\n","\n","preds = []\n","model.eval()\n","torch.set_grad_enabled(False)\n","for batch_id, (input_id,token_type_id,attention_mask) in enumerate(tqdm_notebook(test_dataloader)):\n","    input_id = input_id.long().to(device)\n","    token_type_id = token_type_id.long().to(device)\n","    attention_mask = attention_mask.long().to(device)\n","    outputs = model(input_ids=input_id, token_type_ids=token_type_id, attention_mask=attention_mask)\n","    out = outputs[0]\n","    for inp in out:\n","      preds.append(inp.detach().cpu().numpy())\n","Preds = np.array(preds)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["17a1c797da6a4f84bd18b3fcfec2ebb1","60227edd8eb346ff98e4e1fa2d6f4b2f","9f7d8ac5061a477bbe0097e4464402c9","cd3bed3327934cec8d6bbc71087dad5c","467885d3fa82433d894ab0d8d66701df","a32d945bb77d4a798be443ad5ccbf9ef","60cb718fd1bd46589c9c4a7d2bab442e","dd9536164ece490f83b0e3dead791922","569dbc13eca64ee29d9a33db1cf226a0","45f1855670e246ed97ae4662cbf6b216","34319d539a414a3bb6a24f86cf4bf340","b1cbe5920ac54157890c77efaddea681","22d4628cdfa547beb4d3aea1e5f041d6","eb730a50f95c434e868ff1561a355f78","081ae2fbc26f4716901e18ee295a30c5","24fcb911720540ae9bfc60206566c124","3903b11df6a84182a3aee35ba8b2acc1","6cdeb826e4d84cf9a45e14dd423a8b82","6ea05d1dbf9048e28f0dfbf34420a8b3","cc466820ae5d4b2cab3d062d7dd4ed51","d5d963f301564d9eb439ea09a6a98b41","88973c9f5f54416ba95adf2461a49668","da9e42075b8d4509af3863f27d0318aa","29f0a89e5b9d4ac69e0e4da97c2de45d","63cded615c174082baf81ca089813fea","398459a18b8f45d4b75b8bfe5a27a13e","04f144aa7d394b7db1bd7accff140e06","28d3365ff45b4f12ac401c9db355b3d1","a18bde13957d474581721b45d463cdf0","d26719a9b3dc4080be973619c3dba6f8","d769afa297334c6e9510c47d6f66df6c","90d9f3c910ed4303babc73c22387710b","cc66ad64a16e4ce9bcc49b85d8687fcc","e2f8d435f795402d933dee8fc455cf81","afca902d07804727ad5d33082b878edd","ba6fc22320d94c54b9c10a208189b226","a8892f23c6f841a894702c363dfa9d87","652600245f5340c9a9f7217745a852b7","b460e2a69a4643ca941d9d0ef9fd8313","187bd67c91514fe59b32ffea3642115c","c2a6b76ccc764fe1a27712f66601f3cf","4d53bf4691644931bd60f4769c2563a0","1f2c3c5a86374181bf19a6263999057c","287b1260fdd740379533ed7b35181a82","04d0a7a6e11c496692b3192c535d3cf0","7dadd35bd4f244238d981714174b5766","85d0f2f5e0d34d099d14b5c0d5a8ff97","ead346ca6ca44c2f945d238cab19781f","b5ee066205094ef3a7b17768b684bb7c","e6e65235ccb84ac79644c1bec4f6f84b","385a114083f24ebf8f7e8eff4594653f","dbd983f40fb740cbb1f3e1b10ca11a9a","24480d5247bc4b0b8e44bbe75c3f94dc","f1fd44506cd141df9fa48f12a4f43f95","2d8cf50f340e448f9297e31e836455ae","2684c68039d34e12b03638e0476c746c","34a82862024a417baa6c15437596a5af","b3332a0798f74fa1aae27521821c7302","7f99595d81cc4ca9a3c3c2017c234ee0","472efee92ba243d3b7b348c7ebc07714","c5bf605e479d49ea8dda981efe5f0d96","afd787d059384df6a0d0900f142cdc66","e99bb8af22bd490b98c6fa71466f5013","841e12077abb4de5b3a68dfcb5592957","d3b859d922904f25a6ed1011156b19ae","f10c3848693045fd8107b27bc398d373"]},"id":"Xygnj9A60z6T","executionInfo":{"status":"ok","timestamp":1731900684917,"user_tz":-540,"elapsed":241275,"user":{"displayName":"‍임주현[학생](공과대학 산업경영공학과)","userId":"14719868373122672648"}},"outputId":"cf8dae61-a17f-49e6-b7ec-7062c6e31947"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/546 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"17a1c797da6a4f84bd18b3fcfec2ebb1"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/443M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b1cbe5920ac54157890c77efaddea681"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at klue/roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[31mPlease check your arguments if you have upgraded adabelief-pytorch from version 0.0.5.\n","\u001b[31mModifications to default arguments:\n","\u001b[31m                           eps  weight_decouple    rectify\n","-----------------------  -----  -----------------  ---------\n","adabelief-pytorch=0.0.5  1e-08  False              False\n",">=0.1.0 (Current 0.2.0)  1e-16  True               True\n","\u001b[34mSGD better than Adam (e.g. CNN for Image Classification)    Adam better than SGD (e.g. Transformer, GAN)\n","----------------------------------------------------------  ----------------------------------------------\n","Recommended eps = 1e-8                                      Recommended eps = 1e-16\n","\u001b[34mFor a complete table of recommended hyperparameters, see\n","\u001b[34mhttps://github.com/juntang-zhuang/Adabelief-Optimizer\n","\u001b[32mYou can disable the log message by setting \"print_change_log = False\", though it is recommended to keep as a reminder.\n","\u001b[0m\n","Weight decoupling enabled in AdaBelief\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-24-70fd2a362e84>:47: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n","Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n","  for batch_id, (input_id,token_type_id,attention_mask,label) in enumerate(tqdm_notebook(train_dataloader)):\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/121 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"da9e42075b8d4509af3863f27d0318aa"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["epoch 1 batch id 1 loss 1.048166036605835 train acc 0.6464811783960721\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["epoch 1 train acc 0.7735183861468781\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-24-70fd2a362e84>:47: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n","Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n","  for batch_id, (input_id,token_type_id,attention_mask,label) in enumerate(tqdm_notebook(train_dataloader)):\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/121 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e2f8d435f795402d933dee8fc455cf81"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["epoch 2 batch id 1 loss 0.2178483009338379 train acc 0.9375\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["epoch 2 train acc 0.8635036038221797\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-24-70fd2a362e84>:47: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n","Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n","  for batch_id, (input_id,token_type_id,attention_mask,label) in enumerate(tqdm_notebook(train_dataloader)):\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/121 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"04d0a7a6e11c496692b3192c535d3cf0"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["epoch 3 batch id 1 loss 0.2545599639415741 train acc 0.9042151162790697\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["epoch 3 train acc 0.8971422573765364\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-24-70fd2a362e84>:69: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n","Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n","  for batch_id, (input_id,token_type_id,attention_mask) in enumerate(tqdm_notebook(test_dataloader)):\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/31 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2684c68039d34e12b03638e0476c746c"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n"]}]},{"cell_type":"code","source":["preds = []\n","model.eval()\n","torch.set_grad_enabled(False)\n","for batch_id, (input_id,token_type_id,attention_mask) in enumerate(tqdm_notebook(test_dataloader)):\n","    input_id = input_id.long().to(device)\n","    token_type_id = token_type_id.long().to(device)\n","    attention_mask = attention_mask.long().to(device)\n","    outputs = model(input_ids=input_id, token_type_ids=token_type_id, attention_mask=attention_mask)\n","    out = outputs[0]\n","    for inp in out:\n","      preds.append(inp.detach().cpu().numpy())\n","Preds = np.array(preds)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":120,"referenced_widgets":["ce8d71432f9a41d9be2e171f77038262","edb6aba81f1f450abbbc9e28cd6e89e8","72171b2434904f79af7ffd59598caf6a","8d8aa29f98c247dba927301b00fd8a13","79279cc9d2ec4df6a2f3bcb0c464ce99","adf183da464c4beaa7444b4c8759f091","e6e9f7ff096642089395ffb6ada4ea8f","478992846a964dfa84284a5cd95d7ea5","c44b189d90b84e0da8bc5fa1f6129371","0a6bb807d9294c46a99124dae29ba124","d02d490cdf474dc58028d7f30842696f"]},"id":"8nyZKmzR021B","executionInfo":{"status":"ok","timestamp":1731900760808,"user_tz":-540,"elapsed":6461,"user":{"displayName":"‍임주현[학생](공과대학 산업경영공학과)","userId":"14719868373122672648"}},"outputId":"5d297b7c-5b77-485b-eeed-2c1e2a874db8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-25-2835772bb3d5>:4: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n","Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n","  for batch_id, (input_id,token_type_id,attention_mask) in enumerate(tqdm_notebook(test_dataloader)):\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/31 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ce8d71432f9a41d9be2e171f77038262"}},"metadata":{}}]},{"cell_type":"code","source":["# 모델이 예측한 라벨 도출\n","predicted_label = np.argmax(Preds, axis=1)\n"],"metadata":{"id":"5OcolFL12QN4"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["predicted_label"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PjywLdSQ2SXb","executionInfo":{"status":"ok","timestamp":1731900767425,"user_tz":-540,"elapsed":579,"user":{"displayName":"‍임주현[학생](공과대학 산업경영공학과)","userId":"14719868373122672648"}},"outputId":"ab75b61c-83e9-4741-ca30-6210c392a5ee"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 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1, 1, 0, 0, 2, 1, 0,\n","       0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n","       0, 1, 0, 0, 2, 0, 1, 2, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 2, 0, 1, 0,\n","       0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 2, 1, 1, 1, 0, 0, 1, 0, 1,\n","       1, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 1, 0, 2, 1])"]},"metadata":{},"execution_count":27}]},{"cell_type":"code","source":["from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, \\\n","                            roc_auc_score, confusion_matrix, classification_report, \\\n","                            matthews_corrcoef, cohen_kappa_score, log_loss\n","\n","# Classification Report 저장\n","path2data='./데이터'\n","if not os.path.exists(path2data):\n","    os.mkdir(path2data)\n","CL_REPORT_FILE = \"./데이터/cl_report.csv\"\n","\n","cl_report = classification_report(y_test, predicted_label, output_dict = True)\n","cl_report_df = pd.DataFrame(cl_report).transpose()\n","cl_report_df = cl_report_df.round(3)\n","cl_report_df.to_csv(CL_REPORT_FILE)\n","print(cl_report_df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HrOHPAC_2VCB","executionInfo":{"status":"ok","timestamp":1731900776736,"user_tz":-540,"elapsed":8,"user":{"displayName":"‍임주현[학생](공과대학 산업경영공학과)","userId":"14719868373122672648"}},"outputId":"60fe4d61-c0b2-452c-c829-21bf0e44beb3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision  recall  f1-score  support\n","0                 0.895   0.879     0.887  572.000\n","1                 0.799   0.817     0.808  273.000\n","2                 0.824   0.851     0.837  121.000\n","accuracy          0.858   0.858     0.858    0.858\n","macro avg         0.839   0.849     0.844  966.000\n","weighted avg      0.859   0.858     0.859  966.000\n"]}]},{"cell_type":"code","source":["torch.save(model.state_dict(),'./klue_base_fold3_s.pth')"],"metadata":{"id":"5bNtcvXz9Xwm"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["#여기서부터 데이터 적용. 학습은 위에서 끝"],"metadata":{"id":"GJkTf4IjXQJy"}},{"cell_type":"code","source":["\n","import torch.cuda.amp as amp\n","from transformers import XLMPreTrainedModel, XLMRobertaModel, XLMRobertaConfig, XLMRobertaTokenizer\n","from transformers import XLMRobertaForSequenceClassification, BertForSequenceClassification\n","from transformers import AutoTokenizer\n","from transformers import BertForSequenceClassification, DistilBertForSequenceClassification, XLNetForSequenceClassification,\\\n","XLMRobertaForSequenceClassification, XLMForSequenceClassification, RobertaForSequenceClassification\n","from transformers import AdamW\n","from transformers import get_linear_schedule_with_warmup"],"metadata":{"id":"QKoXYUskXM2V","executionInfo":{"status":"ok","timestamp":1731941324993,"user_tz":-540,"elapsed":13988,"user":{"displayName":"‍임주현[학생](공과대학 산업경영공학과)","userId":"14719868373122672648"}}},"execution_count":1,"outputs":[]},{"cell_type":"code","source":["tf.random.set_seed(1234)\n","np.random.seed(1234)\n","\n","class args:\n","    # ---- factor ---- #\n","    debug=False\n","    amp = True\n","    gpu = '0'\n","\n","    epochs=5\n","    batch_size=32\n","    weight_decay=1e-6\n","    n_fold=5\n","    fold=3 # [0, 1, 2, 3, 4] # 원래는 3\n","\n","    exp_name = 'model_f'\n","    dir_ = f'./saved_models/'\n","    pt = 'mz_model'\n","    max_len = 128\n","\n","    start_lr = 1e-5#1e-3,5e-5\n","    min_lr=1e-6\n","    # ---- Dataset ---- #\n","\n","    # ---- Else ---- #\n","    num_workers=8\n","    seed=2021\n","    scheduler = None#'get_linear_schedule_with_warmup'\n"],"metadata":{"id":"lgep8lk_bQC_","colab":{"base_uri":"https://localhost:8080/","height":207},"executionInfo":{"status":"error","timestamp":1731941324993,"user_tz":-540,"elapsed":7,"user":{"displayName":"‍임주현[학생](공과대학 산업경영공학과)","userId":"14719868373122672648"}},"outputId":"71648ece-9ac8-433a-9e45-499420183505"},"execution_count":2,"outputs":[{"output_type":"error","ename":"NameError","evalue":"name 'tf' is not defined","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-2-185582aa2a58>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_seed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1234\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1234\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;31m# ---- factor ---- #\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'tf' is not defined"]}]},{"cell_type":"code","source":["os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu\n","device = torch.device(f'cuda' if torch.cuda.is_available() else 'cpu')\n","\n","tokenizer = AutoTokenizer.from_pretrained(\"klue/roberta-base\", cache_dir = 'bert_ckpt', do_lower_case = False)"],"metadata":{"id":"abi0wr-RbSWA","executionInfo":{"status":"aborted","timestamp":1731941324994,"user_tz":-540,"elapsed":5,"user":{"displayName":"‍임주현[학생](공과대학 산업경영공학과)","userId":"14719868373122672648"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class TestDataset(Dataset):\n","    def __init__(self, df):\n","        self.df_data = df\n","    def __getitem__(self, index):\n","        # get the sentence from the dataframe\n","        sentence = self.df_data.loc[index, 'kor_sentence']\n","        encoded_dict = tokenizer(\n","          text = sentence,\n","          add_special_tokens = True,\n","          max_length = MAX_LEN,\n","          pad_to_max_length = True,\n","          truncation=True,           # Pad & truncate all sentences.\n","          return_tensors=\"pt\")\n","\n","        padded_token_list = encoded_dict['input_ids'][0]\n","        token_type_id = encoded_dict['token_type_ids'][0]\n","        att_mask = encoded_dict['attention_mask'][0]\n","        sample = (padded_token_list, token_type_id , att_mask)\n","        return sample\n","    def __len__(self):\n","        return len(self.df_data)\n","\n"],"metadata":{"id":"zcDM7oN7bToA"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 데이터 불러오기 및 전처리\n","df = pd.read_csv(\"\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":139},"id":"QPqLcRmm46a0","executionInfo":{"status":"error","timestamp":1731910156762,"user_tz":-540,"elapsed":497,"user":{"displayName":"‍임주현[학생](공과대학 산업경영공학과)","userId":"14719868373122672648"}},"outputId":"cfea65b5-839a-405b-ee9a-07b41b8484de"},"execution_count":null,"outputs":[{"output_type":"error","ename":"NameError","evalue":"name 'pd' is not defined","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-1-186c70b8a655>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/KHUDA 금융/심화트랙/데이터/현대차_daily_news_titles .csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"]}]},{"cell_type":"code","source":["test_data = TestDataset(df)\n","\n","test_dataloader = torch.utils.data.DataLoader(test_data,\n","                                        batch_size=args.batch_size,\n","                                        shuffle=False,\n","                                      num_workers=0)"],"metadata":{"id":"H0CdeSF546Sh"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","def do_predict(net, valid_loader):\n","\n","    val_loss = 0\n","    pred_lst = []\n","    logit=[]\n","    net.eval()\n","    for batch_id, (input_id,token_type_id,attention_mask) in enumerate(tqdm_notebook(valid_loader)):\n","        input_id = input_id.long().to(device)\n","        token_type_id = token_type_id.long().to(device)\n","        attention_mask = attention_mask.long().to(device)\n","\n","        with torch.no_grad():\n","            if args.amp:\n","                with amp.autocast():\n","                    # output\n","                    output = net(input_ids=input_id, token_type_ids=token_type_id, attention_mask=attention_mask)[0]\n","\n","            else:\n","                output = net(outputs = model(input_ids=input_id, token_type_ids=token_type_id, attention_mask=attention_mask))\n","\n","            pred_lst.extend(output.argmax(dim=1).tolist())\n","            logit.extend(output.tolist())\n","\n","    return pred_lst,logit\n","\n","def run_predict(model_path,test_dataloader):\n","\n","\n","    print('set testloader')\n","    ## net ----------------------------------------\n","    scaler = amp.GradScaler()\n","\n","    net = RobertaForSequenceClassification.from_pretrained('klue/roberta-base', num_labels = 3)\n","\n","\n","    net.to(device)\n","\n","    if len(args.gpu)>1:\n","        net = nn.DataParallel(net)\n","\n","    f = torch.load(model_path)\n","    net.load_state_dict(f, strict=True)  # True\n","    print('load saved models')\n","    # ------------------------\n","    # validation\n","    preds, logit = do_predict(net, test_dataloader) #outputs\n","\n","    print('complete predict')\n","\n","    return preds, np.array(logit)"],"metadata":{"id":"dKc9tH-0bat6"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["preds1, logit1 = run_predict('./klue_base_fold3_s.pth',test_dataloader)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":341,"referenced_widgets":["734eb82841134bdfa9244fa0eedca60b","4486f75f441e4872970d8f3d869f9f27","6f2a88dd024d4a99bf69511351d5d50d","6e11cd45d8694d538949a65fccfcc976","b98351841ca04b19b2b0c5a5fed9dda0","217f61b562e24d679eccac07593b0bc9","8afb42ae78254049a79a9fd6f6c5a0df","3c61f314dcbc44bc853d2e664684d2b2","a7f14f794d40458a8dead66504aa1e8e","ac9529257f8240628906a3df948e964c","3c75f82b465f4cc5b9ee5a7cfcfa714e"]},"id":"Cg-nzVG3bczu","executionInfo":{"status":"ok","timestamp":1731904408437,"user_tz":-540,"elapsed":7103,"user":{"displayName":"‍임주현[학생](공과대학 산업경영공학과)","userId":"14719868373122672648"}},"outputId":"ab45bc00-4b73-4ec5-a7e1-08566da5652f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["set testloader\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-63-08a68ad77adc>:31: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n","  scaler = amp.GradScaler()\n","Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at klue/roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","<ipython-input-63-08a68ad77adc>:41: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n","  f = torch.load(model_path)\n"]},{"output_type":"stream","name":"stdout","text":["load saved models\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-63-08a68ad77adc>:7: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n","Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n","  for batch_id, (input_id,token_type_id,attention_mask) in enumerate(tqdm_notebook(valid_loader)):\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/93 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"734eb82841134bdfa9244fa0eedca60b"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2834: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n","  warnings.warn(\n","<ipython-input-63-08a68ad77adc>:14: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n","  with amp.autocast():\n"]},{"output_type":"stream","name":"stdout","text":["complete predict\n"]}]},{"cell_type":"code","source":["print(type(logit1))  # 타입 확인\n","print(logit1.dtype)  # 데이터 타입\n","print(logit1.shape)  # 배열의 차원 (행, 열)\n","print(logit1.size)   # 총 원소 수\n","print(logit1.ndim)   # 차원 수"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yZWVADm2beDo","executionInfo":{"status":"ok","timestamp":1731904586151,"user_tz":-540,"elapsed":5,"user":{"displayName":"‍임주현[학생](공과대학 산업경영공학과)","userId":"14719868373122672648"}},"outputId":"a24e503c-304c-49eb-8974-2bb11557502c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'numpy.ndarray'>\n","float64\n","(2967, 3)\n","8901\n","2\n"]}]},{"cell_type":"code","source":["print(logit1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GCsHmwfMkvon","executionInfo":{"status":"ok","timestamp":1731904867609,"user_tz":-540,"elapsed":4,"user":{"displayName":"‍임주현[학생](공과대학 산업경영공학과)","userId":"14719868373122672648"}},"outputId":"7c98f223-8b32-463b-fb53-7f0923b3ef8f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[-0.57861328  2.734375   -1.51660156]\n"," [-0.32470703  2.39257812 -1.59082031]\n"," [ 2.17578125 -0.28491211 -1.96386719]\n"," ...\n"," [ 1.43945312  1.29492188 -2.41015625]\n"," [ 2.61523438 -0.74414062 -2.00195312]\n"," [-0.64257812 -1.79296875  2.21679688]]\n"]}]},{"cell_type":"code","source":["import numpy as np\n","\n","# logit1: 각 클래스(긍정, 중립, 부정)에 대한 정규화되지 않은 점수 리스트\n","# 예제 logit1 (실제 데이터로 교체하세요)\n","\n","# 소프트맥스 함수 정의\n","def softmax(logits):\n","    exp_values = np.exp(logits - np.max(logits, axis=-1, keepdims=True))  # 안정성을 위해 max 값 빼기\n","    probabilities = exp_values / np.sum(exp_values, axis=-1, keepdims=True)\n","    return probabilities\n","\n","# logit1을 소프트맥스 변환\n","new_logit1 = [softmax(logit) for logit in logit1]\n","\n","# 결과 확인\n","print(\"Softmax 변환 후 확률:\")\n","for i, probs in enumerate(new_logit1):\n","    print(f\"Logit {i + 1}: {probs}\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0a8YsnTrnYxi","executionInfo":{"status":"ok","timestamp":1731905575276,"user_tz":-540,"elapsed":2812,"user":{"displayName":"‍임주현[학생](공과대학 산업경영공학과)","userId":"14719868373122672648"}},"outputId":"e00e0960-5bb2-41ee-efc1-b6cce48da4f6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Softmax 변환 후 확률:\n","Logit 1: [0.03465184 0.95178493 0.01356323]\n","Logit 2: [0.06089729 0.92193422 0.01716849]\n","Logit 3: [0.90801435 0.07752239 0.01446326]\n","Logit 4: [0.01957411 0.96738672 0.01303917]\n","Logit 5: [0.90801435 0.07752239 0.01446326]\n","Logit 6: [0.02266249 0.96504617 0.01229134]\n","Logit 7: [0.02213297 0.964377   0.01349003]\n","Logit 8: [0.05552068 0.92789423 0.01658508]\n","Logit 9: [0.21469549 0.77428153 0.01102298]\n","Logit 10: [0.45602827 0.53159139 0.01238034]\n","Logit 11: [0.08083315 0.0161716  0.90299525]\n","Logit 12: [0.06554225 0.92431903 0.01013872]\n","Logit 13: [0.18046593 0.80681999 0.01271408]\n","Logit 14: [0.02259963 0.96566438 0.01173599]\n","Logit 15: [0.45476933 0.53480366 0.01042701]\n","Logit 16: [0.87501935 0.1144907  0.01048995]\n","Logit 17: [0.25189618 0.72995426 0.01814955]\n","Logit 18: [0.15802543 0.01914435 0.82283022]\n","Logit 19: [0.02507126 0.96263218 0.01229656]\n","Logit 20: [0.31064741 0.67752314 0.01182945]\n","Logit 21: [0.74418191 0.24588492 0.00993317]\n","Logit 22: [0.76987319 0.21832219 0.01180463]\n","Logit 23: [0.48374045 0.49521203 0.02104752]\n","Logit 24: [0.06289809 0.92676618 0.01033574]\n","Logit 25: [0.86241959 0.09878975 0.03879066]\n","Logit 26: [0.13839942 0.85070192 0.01089866]\n","Logit 27: [0.80015426 0.17803798 0.02180775]\n","Logit 28: [0.89227322 0.0941253  0.01360148]\n","Logit 29: [0.85152031 0.13887119 0.0096085 ]\n","Logit 30: [0.05181725 0.93203618 0.01614657]\n","Logit 31: [0.91668698 0.07372783 0.00958519]\n","Logit 32: [0.06554894 0.91721958 0.01723147]\n","Logit 33: [0.89817606 0.09128524 0.01053869]\n","Logit 34: [0.85424879 0.13384827 0.01190293]\n","Logit 35: [0.88768328 0.10190115 0.01041558]\n","Logit 36: [0.39942207 0.08921019 0.51136774]\n","Logit 37: [0.05815767 0.93131291 0.01052942]\n","Logit 38: [0.03938782 0.01560631 0.94500586]\n","Logit 39: [0.53970916 0.01914733 0.44114351]\n","Logit 40: [0.89368801 0.08842231 0.01788968]\n","Logit 41: [0.45845267 0.52408093 0.0174664 ]\n","Logit 42: [0.52364787 0.02538006 0.45097206]\n","Logit 43: [0.7491065  0.03237154 0.21852196]\n","Logit 44: [0.26295075 0.72709459 0.00995466]\n","Logit 45: [0.18907971 0.80151575 0.00940454]\n","Logit 46: [0.14230875 0.84947636 0.00821489]\n","Logit 47: [0.05074233 0.93708703 0.01217064]\n","Logit 48: [0.45361953 0.50235628 0.04402419]\n","Logit 49: [0.83249424 0.03047203 0.13703373]\n","Logit 50: [0.03786932 0.95031884 0.01181184]\n","Logit 51: [0.94021806 0.04975679 0.01002514]\n","Logit 52: [0.16451675 0.25704223 0.57844102]\n","Logit 53: [0.08584761 0.01653701 0.89761538]\n","Logit 54: [0.29305667 0.69617451 0.01076882]\n","Logit 55: [0.03016009 0.94931265 0.02052726]\n","Logit 56: [0.40855828 0.02642613 0.56501559]\n","Logit 57: [0.03969859 0.94898021 0.0113212 ]\n","Logit 58: [0.45330162 0.51895213 0.02774625]\n","Logit 59: [0.36105968 0.62568557 0.01325475]\n","Logit 60: [0.80807425 0.18296637 0.00895938]\n","Logit 61: [0.92231918 0.06889979 0.00878104]\n","Logit 62: [0.08037484 0.90979092 0.00983425]\n","Logit 63: [0.14625094 0.84243832 0.01131075]\n","Logit 64: [0.91913042 0.07153633 0.00933326]\n","Logit 65: [0.23376491 0.65793491 0.10830018]\n","Logit 66: [0.35643727 0.6078023  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0.01712336]\n","Logit 927: [0.88816677 0.10471266 0.00712057]\n","Logit 928: [0.84150438 0.14637441 0.01212121]\n","Logit 929: [0.72798806 0.25492471 0.01708723]\n","Logit 930: [0.9627271  0.02759195 0.00968096]\n","Logit 931: [0.86043982 0.1258948  0.01366538]\n","Logit 932: [0.1734228  0.81691678 0.00966042]\n","Logit 933: [0.32680796 0.66210715 0.0110849 ]\n","Logit 934: [0.78743547 0.02970863 0.1828559 ]\n","Logit 935: [0.79380902 0.19453127 0.01165972]\n","Logit 936: [0.58560224 0.4005177  0.01388006]\n","Logit 937: [0.89668964 0.09483977 0.00847059]\n","Logit 938: [0.05059464 0.93801651 0.01138885]\n","Logit 939: [0.79648268 0.02709491 0.17642241]\n","Logit 940: [0.8646849  0.02340595 0.11190915]\n","Logit 941: [0.8795935  0.10460778 0.01579872]\n","Logit 942: [0.06937789 0.91946935 0.01115276]\n","Logit 943: [0.01835664 0.97045499 0.01118837]\n","Logit 944: [0.84724048 0.14014368 0.01261584]\n","Logit 945: [0.19307876 0.79406134 0.0128599 ]\n","Logit 946: [0.04262156 0.94551103 0.01186741]\n","Logit 947: [0.09607254 0.89344429 0.01048317]\n","Logit 948: [0.32710072 0.65979442 0.01310486]\n","Logit 949: [0.51453901 0.47633574 0.00912525]\n","Logit 950: [0.89606774 0.09492161 0.00901064]\n","Logit 951: [0.03262825 0.95446895 0.0129028 ]\n","Logit 952: [0.03595106 0.95056902 0.01347992]\n","Logit 953: [0.02060526 0.96792618 0.01146856]\n","Logit 954: [0.0624902  0.91582395 0.02168585]\n","Logit 955: [0.02571093 0.9624428  0.01184628]\n","Logit 956: [0.70324241 0.28206215 0.01469545]\n","Logit 957: [0.26070523 0.72902694 0.01026783]\n","Logit 958: [0.45562263 0.02834133 0.51603604]\n","Logit 959: [0.27761515 0.02582223 0.69656262]\n","Logit 960: [0.14759906 0.07544146 0.77695948]\n","Logit 961: [0.09534652 0.89342882 0.01122466]\n","Logit 962: [0.96410249 0.02276226 0.01313525]\n","Logit 963: [0.87196171 0.11213002 0.01590827]\n","Logit 964: [0.89314038 0.06295373 0.04390589]\n","Logit 965: [0.73406258 0.21850356 0.04743387]\n","Logit 966: [0.79978701 0.18830376 0.01190924]\n","Logit 967: [0.08279432 0.02119596 0.89600972]\n","Logit 968: [0.07216571 0.02032548 0.90750881]\n","Logit 969: [0.24792039 0.56195473 0.19012487]\n","Logit 970: [0.0395441  0.94875527 0.01170063]\n","Logit 971: [0.3270238  0.04431192 0.62866429]\n","Logit 972: [0.95867679 0.03134779 0.00997542]\n","Logit 973: [0.36539976 0.02207292 0.61252732]\n","Logit 974: [0.74372394 0.2459737  0.01030236]\n","Logit 975: [0.92244692 0.06845659 0.0090965 ]\n","Logit 976: [0.05330085 0.93035976 0.0163394 ]\n","Logit 977: [0.10882119 0.88117723 0.01000158]\n","Logit 978: [0.19528296 0.02076944 0.78394759]\n","Logit 979: [0.02767708 0.95537946 0.01694346]\n","Logit 980: [0.5451767  0.44626576 0.00855754]\n","Logit 981: [0.64800379 0.32871319 0.02328303]\n","Logit 982: [0.74049137 0.23598289 0.02352574]\n","Logit 983: [0.62102833 0.34296442 0.03600726]\n","Logit 984: [0.12958631 0.85392657 0.01648712]\n","Logit 985: [0.51157691 0.46625118 0.02217191]\n","Logit 986: [0.1445249  0.838411   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0.01405946]\n","Logit 1026: [0.12221397 0.01861444 0.8591716 ]\n","Logit 1027: [0.86730443 0.12412613 0.00856944]\n","Logit 1028: [0.85817154 0.13231363 0.00951482]\n","Logit 1029: [0.13309832 0.02661809 0.84028359]\n","Logit 1030: [0.46275189 0.03835775 0.49889036]\n","Logit 1031: [0.11715027 0.86130835 0.02154139]\n","Logit 1032: [0.66724114 0.32328754 0.00947132]\n","Logit 1033: [0.1429656 0.8324081 0.0246263]\n","Logit 1034: [0.9513801  0.03978927 0.00883063]\n","Logit 1035: [0.96609788 0.02121919 0.01268293]\n","Logit 1036: [0.19100079 0.79710668 0.01189253]\n","Logit 1037: [0.94513637 0.02598655 0.02887707]\n","Logit 1038: [0.5497166  0.03330448 0.41697892]\n","Logit 1039: [0.37307698 0.02489715 0.60202586]\n","Logit 1040: [0.88280437 0.10826619 0.00892944]\n","Logit 1041: [0.14090252 0.8468515  0.01224597]\n","Logit 1042: [0.72677271 0.01879029 0.254437  ]\n","Logit 1043: [0.1200081  0.87002005 0.00997185]\n","Logit 1044: [0.56974361 0.41887362 0.01138277]\n","Logit 1045: 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0.01055292]\n","Logit 1572: [0.8945871  0.09720616 0.00820674]\n","Logit 1573: [0.90689385 0.08462242 0.00848373]\n","Logit 1574: [0.90564603 0.08566405 0.00868992]\n","Logit 1575: [0.80076912 0.18697648 0.0122544 ]\n","Logit 1576: [0.89984045 0.09039479 0.00976476]\n","Logit 1577: [0.0325673 0.9522208 0.0152119]\n","Logit 1578: [0.03846403 0.9479611  0.01357486]\n","Logit 1579: [0.05950062 0.9277969  0.01270248]\n","Logit 1580: [0.3231126  0.66330782 0.01357958]\n","Logit 1581: [0.3822532  0.02483357 0.59291323]\n","Logit 1582: [0.09572242 0.89214641 0.01213118]\n","Logit 1583: [0.18345314 0.80195854 0.01458832]\n","Logit 1584: [0.23240579 0.75536107 0.01223314]\n","Logit 1585: [0.02728281 0.96128277 0.01143442]\n","Logit 1586: [0.95867125 0.02811354 0.01321521]\n","Logit 1587: [0.77828703 0.21370892 0.00800404]\n","Logit 1588: [0.59960494 0.38600153 0.01439353]\n","Logit 1589: [0.03939176 0.95111845 0.00948979]\n","Logit 1590: [0.07194291 0.91559491 0.01246219]\n","Logit 1591: 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0.01845544]\n","Logit 1689: [0.06076797 0.66881026 0.27042176]\n","Logit 1690: [0.87649176 0.11100576 0.01250248]\n","Logit 1691: [0.85761565 0.13200214 0.01038221]\n","Logit 1692: [0.0778456  0.02317461 0.89897979]\n","Logit 1693: [0.19048596 0.78320685 0.02630719]\n","Logit 1694: [0.58196494 0.40272186 0.0153132 ]\n","Logit 1695: [0.80219109 0.18749151 0.01031739]\n","Logit 1696: [0.05749755 0.03824556 0.90425688]\n","Logit 1697: [0.07942095 0.90845323 0.01212582]\n","Logit 1698: [0.10496854 0.87186501 0.02316645]\n","Logit 1699: [0.60359523 0.3747808  0.02162397]\n","Logit 1700: [0.96376712 0.01927368 0.0169592 ]\n","Logit 1701: [0.06682682 0.92094056 0.01223262]\n","Logit 1702: [0.06617749 0.92161672 0.01220579]\n","Logit 1703: [0.25502428 0.71036067 0.03461505]\n","Logit 1704: [0.08083466 0.89850388 0.02066146]\n","Logit 1705: [0.04269821 0.93412116 0.02318063]\n","Logit 1706: [0.06003869 0.92867487 0.01128644]\n","Logit 1707: [0.53062717 0.46056688 0.00880594]\n","Logit 1708: [0.47725882 0.49869977 0.02404141]\n","Logit 1709: [0.89306546 0.09444204 0.0124925 ]\n","Logit 1710: [0.93975836 0.0400002  0.02024144]\n","Logit 1711: [0.06362016 0.9260321  0.01034774]\n","Logit 1712: [0.62826297 0.35036464 0.02137238]\n","Logit 1713: [0.06561594 0.92208825 0.0122958 ]\n","Logit 1714: [0.53634474 0.44900891 0.01464635]\n","Logit 1715: [0.70136131 0.2895296  0.00910909]\n","Logit 1716: [0.96133186 0.02425523 0.01441291]\n","Logit 1717: [0.03816951 0.94808046 0.01375003]\n","Logit 1718: [0.1582887  0.82674535 0.01496596]\n","Logit 1719: [0.93012586 0.06082647 0.00904767]\n","Logit 1720: [0.5628089  0.42649318 0.01069792]\n","Logit 1721: [0.69937953 0.29012467 0.0104958 ]\n","Logit 1722: [0.63862317 0.35096333 0.01041351]\n","Logit 1723: [0.04289309 0.94021927 0.01688764]\n","Logit 1724: [0.96486827 0.02741133 0.0077204 ]\n","Logit 1725: [0.9041572  0.08239258 0.01345022]\n","Logit 1726: [0.12845069 0.01745142 0.85409789]\n","Logit 1727: [0.5310469  0.45378512 0.01516797]\n","Logit 1728: [0.75752173 0.22277789 0.01970038]\n","Logit 1729: [0.89569545 0.09204157 0.01226298]\n","Logit 1730: [0.02294379 0.9637579  0.01329831]\n","Logit 1731: [0.96097713 0.02479703 0.01422584]\n","Logit 1732: [0.47912305 0.02315411 0.49772284]\n","Logit 1733: [0.03713779 0.94848828 0.01437393]\n","Logit 1734: [0.03791906 0.94277986 0.01930108]\n","Logit 1735: [0.45804466 0.52157314 0.0203822 ]\n","Logit 1736: [0.91190785 0.07538581 0.01270634]\n","Logit 1737: [0.03712472 0.94630447 0.01657081]\n","Logit 1738: [0.86262156 0.12737485 0.0100036 ]\n","Logit 1739: [0.06663968 0.90721922 0.02614111]\n","Logit 1740: [0.96136154 0.02666601 0.01197245]\n","Logit 1741: [0.10078896 0.87676312 0.02244792]\n","Logit 1742: [0.03428957 0.9515415  0.01416894]\n","Logit 1743: [0.06021811 0.02008209 0.9196998 ]\n","Logit 1744: [0.04458951 0.93812711 0.01728337]\n","Logit 1745: [0.11061299 0.37916488 0.51022212]\n","Logit 1746: [0.56176697 0.3919851  0.04624793]\n","Logit 1747: [0.41854448 0.56596995 0.01548556]\n","Logit 1748: [0.04786754 0.02661629 0.92551617]\n","Logit 1749: [0.37165431 0.61066357 0.01768212]\n","Logit 1750: [0.14763094 0.83475427 0.01761479]\n","Logit 1751: [0.07144871 0.91576623 0.01278505]\n","Logit 1752: [0.09314499 0.89633789 0.01051713]\n","Logit 1753: [0.12027358 0.01741399 0.86231243]\n","Logit 1754: [0.84048685 0.12984034 0.02967281]\n","Logit 1755: [0.09678954 0.89125596 0.01195451]\n","Logit 1756: [0.19273446 0.79187166 0.01539387]\n","Logit 1757: [0.21061275 0.77416029 0.01522697]\n","Logit 1758: [0.06268928 0.02237439 0.91493633]\n","Logit 1759: [0.92084519 0.05225582 0.02689899]\n","Logit 1760: [0.04321369 0.94586018 0.01092613]\n","Logit 1761: [0.86153339 0.1232024  0.01526421]\n","Logit 1762: [0.96224175 0.02720356 0.01055469]\n","Logit 1763: [0.81650252 0.17499314 0.00850434]\n","Logit 1764: [0.05282793 0.9291103  0.01806177]\n","Logit 1765: [0.65972828 0.33011638 0.01015534]\n","Logit 1766: [0.25858072 0.72875727 0.01266201]\n","Logit 1767: [0.77047706 0.21032838 0.01919456]\n","Logit 1768: [0.91902875 0.06094297 0.02002829]\n","Logit 1769: [0.82538334 0.16524835 0.00936831]\n","Logit 1770: [0.96136154 0.02666601 0.01197245]\n","Logit 1771: [0.17281122 0.79994935 0.02723943]\n","Logit 1772: [0.30690062 0.68422157 0.00887781]\n","Logit 1773: [0.33919549 0.64085747 0.01994704]\n","Logit 1774: [0.71499828 0.27687044 0.00813127]\n","Logit 1775: [0.07942371 0.90133238 0.01924391]\n","Logit 1776: [0.94842093 0.04140703 0.01017203]\n","Logit 1777: [0.71434214 0.27729252 0.00836534]\n","Logit 1778: [0.49133501 0.48089157 0.02777342]\n","Logit 1779: [0.93539817 0.02148886 0.04311297]\n","Logit 1780: [0.15491038 0.82525988 0.01982974]\n","Logit 1781: [0.07621404 0.91196472 0.01182124]\n","Logit 1782: [0.03585625 0.94991589 0.01422785]\n","Logit 1783: [0.59977377 0.38875868 0.01146755]\n","Logit 1784: [0.08441582 0.90479016 0.01079402]\n","Logit 1785: [0.49550027 0.49501662 0.00948312]\n","Logit 1786: [0.50686101 0.03366042 0.45947857]\n","Logit 1787: [0.17267094 0.81337511 0.01395395]\n","Logit 1788: [0.47709625 0.51134995 0.01155381]\n","Logit 1789: [0.06080359 0.92456978 0.01462663]\n","Logit 1790: [0.87181461 0.12090328 0.00728211]\n","Logit 1791: [0.2712145  0.03367408 0.69511142]\n","Logit 1792: [0.93206751 0.05276369 0.01516881]\n","Logit 1793: [0.15451861 0.83654424 0.00893715]\n","Logit 1794: [0.07103473 0.91380044 0.01516484]\n","Logit 1795: [0.07864328 0.90741592 0.0139408 ]\n","Logit 1796: [0.81999029 0.1642087  0.01580101]\n","Logit 1797: [0.83039664 0.16192574 0.00767762]\n","Logit 1798: [0.37762582 0.60344434 0.01892984]\n","Logit 1799: [0.72097055 0.02803708 0.25099236]\n","Logit 1800: [0.5434583  0.44791058 0.00863112]\n","Logit 1801: [0.25890415 0.73288239 0.00821346]\n","Logit 1802: [0.08409554 0.90207279 0.01383167]\n","Logit 1803: [0.50980811 0.4812646  0.00892729]\n","Logit 1804: [0.41764144 0.57196419 0.01039437]\n","Logit 1805: [0.06008427 0.93199291 0.00792282]\n","Logit 1806: [0.92294688 0.06861922 0.0084339 ]\n","Logit 1807: [0.73472183 0.25391307 0.01136509]\n","Logit 1808: [0.06671935 0.92125705 0.01202361]\n","Logit 1809: [0.7038421  0.23810023 0.05805767]\n","Logit 1810: [0.52669775 0.44700258 0.02629967]\n","Logit 1811: [0.52303069 0.04187704 0.43509227]\n","Logit 1812: [0.02960337 0.01930094 0.95109569]\n","Logit 1813: [0.03276567 0.01991224 0.94732209]\n","Logit 1814: [0.82254077 0.01776868 0.15969055]\n","Logit 1815: [0.4307896  0.55966509 0.00954531]\n","Logit 1816: [0.35521402 0.63261935 0.01216663]\n","Logit 1817: [0.95419828 0.02532939 0.02047234]\n","Logit 1818: [0.89501916 0.09162749 0.01335335]\n","Logit 1819: [0.9615273  0.02646305 0.01200965]\n","Logit 1820: [0.05730138 0.90691029 0.03578832]\n","Logit 1821: [0.86856094 0.11731758 0.01412148]\n","Logit 1822: [0.94943649 0.04272544 0.00783808]\n","Logit 1823: [0.7038421  0.23810023 0.05805767]\n","Logit 1824: [0.03436651 0.9518158  0.01381769]\n","Logit 1825: [0.08353579 0.89568573 0.02077848]\n","Logit 1826: [0.04651076 0.937392   0.01609725]\n","Logit 1827: [0.84364233 0.14678212 0.00957555]\n","Logit 1828: [0.37762582 0.60344434 0.01892984]\n","Logit 1829: [0.19980545 0.78486225 0.01533229]\n","Logit 1830: [0.75979999 0.22154669 0.01865332]\n","Logit 1831: [0.34124761 0.65010872 0.00864367]\n","Logit 1832: [0.91976098 0.0604283  0.01981072]\n","Logit 1833: [0.88964831 0.09715602 0.01319567]\n","Logit 1834: [0.96239411 0.02478514 0.01282075]\n","Logit 1835: [0.29596677 0.68546054 0.01857269]\n","Logit 1836: [0.31868647 0.66647373 0.01483981]\n","Logit 1837: [0.03504595 0.95325519 0.01169887]\n","Logit 1838: [0.10189295 0.88010664 0.01800041]\n","Logit 1839: [0.09314499 0.89633789 0.01051713]\n","Logit 1840: [0.37762582 0.60344434 0.01892984]\n","Logit 1841: [0.11636924 0.86935762 0.01427314]\n","Logit 1842: [0.45484137 0.02189498 0.52326365]\n","Logit 1843: [0.11288428 0.87349798 0.01361774]\n","Logit 1844: [0.28777676 0.02574825 0.68647499]\n","Logit 1845: [0.41702046 0.02943712 0.55354243]\n","Logit 1846: [0.06594161 0.91609247 0.01796592]\n","Logit 1847: [0.28225315 0.70337509 0.01437176]\n","Logit 1848: [0.76653945 0.22460585 0.0088547 ]\n","Logit 1849: [0.91687339 0.06738171 0.0157449 ]\n","Logit 1850: [0.40863696 0.17558102 0.41578202]\n","Logit 1851: [0.18213594 0.80597925 0.01188481]\n","Logit 1852: [0.19528208 0.02981252 0.7749054 ]\n","Logit 1853: [0.92100588 0.03491811 0.04407601]\n","Logit 1854: [0.43245729 0.55042709 0.01711562]\n","Logit 1855: [0.94716774 0.04155473 0.01127753]\n","Logit 1856: [0.76177726 0.22496113 0.0132616 ]\n","Logit 1857: [0.08583009 0.90281623 0.01135369]\n","Logit 1858: [0.40306396 0.58132281 0.01561323]\n","Logit 1859: [0.62270425 0.36127451 0.01602124]\n","Logit 1860: [0.36869385 0.61865313 0.01265302]\n","Logit 1861: [0.36664594 0.62064784 0.01270622]\n","Logit 1862: [0.32640658 0.66258682 0.0110066 ]\n","Logit 1863: [0.5841527  0.39448626 0.02136104]\n","Logit 1864: [0.96849405 0.02079936 0.01070659]\n","Logit 1865: [0.94650493 0.04076226 0.01273281]\n","Logit 1866: [0.74856732 0.23878964 0.01264303]\n","Logit 1867: [0.93322909 0.05850542 0.00826548]\n","Logit 1868: [0.83261604 0.15298789 0.01439607]\n","Logit 1869: [0.15542403 0.83184599 0.01272998]\n","Logit 1870: [0.86519638 0.03827483 0.09652879]\n","Logit 1871: [0.08248968 0.90422873 0.01328159]\n","Logit 1872: [0.09314499 0.89633789 0.01051713]\n","Logit 1873: [0.03565052 0.95233788 0.0120116 ]\n","Logit 1874: [0.2698347  0.70573183 0.02443347]\n","Logit 1875: [0.7038421  0.23810023 0.05805767]\n","Logit 1876: [0.79462642 0.04851988 0.1568537 ]\n","Logit 1877: [0.89617939 0.06930869 0.03451192]\n","Logit 1878: [0.45087059 0.04343809 0.50569132]\n","Logit 1879: [0.80864679 0.0422335  0.14911971]\n","Logit 1880: [0.95061676 0.03921749 0.01016576]\n","Logit 1881: [0.07046543 0.9195655  0.00996907]\n","Logit 1882: [0.13367864 0.65542406 0.21089731]\n","Logit 1883: [0.85929934 0.12298797 0.01771269]\n","Logit 1884: [0.94236962 0.04767994 0.00995044]\n","Logit 1885: [0.92859427 0.05167634 0.01972939]\n","Logit 1886: [0.29817214 0.68252282 0.01930504]\n","Logit 1887: [0.19619896 0.76825328 0.03554776]\n","Logit 1888: [0.92014438 0.06926798 0.01058764]\n","Logit 1889: [0.96082843 0.01934671 0.01982486]\n","Logit 1890: [0.71249795 0.27779474 0.0097073 ]\n","Logit 1891: [0.61908408 0.03919211 0.3417238 ]\n","Logit 1892: [0.76133517 0.22461112 0.01405371]\n","Logit 1893: [0.27104696 0.71971448 0.00923856]\n","Logit 1894: [0.02950843 0.95594789 0.01454368]\n","Logit 1895: [0.05166843 0.93117625 0.01715531]\n","Logit 1896: [0.03462484 0.95150787 0.01386728]\n","Logit 1897: [0.07218497 0.9135314  0.01428364]\n","Logit 1898: [0.84458798 0.1352167  0.02019531]\n","Logit 1899: [0.21232644 0.02174884 0.76592472]\n","Logit 1900: [0.14355762 0.02007697 0.83636541]\n","Logit 1901: [0.2833306  0.70227795 0.01439145]\n","Logit 1902: [0.11525701 0.87130459 0.0134384 ]\n","Logit 1903: [0.56228319 0.42113065 0.01658615]\n","Logit 1904: [0.42011609 0.55061668 0.02926723]\n","Logit 1905: [0.03983999 0.94934256 0.01081745]\n","Logit 1906: [0.42277135 0.55247579 0.02475286]\n","Logit 1907: [0.6521301  0.32583673 0.02203316]\n","Logit 1908: [0.25389943 0.7343237  0.01177687]\n","Logit 1909: [0.11731303 0.87171371 0.01097326]\n","Logit 1910: [0.91274202 0.07928372 0.00797426]\n","Logit 1911: [0.50770467 0.46114315 0.03115218]\n","Logit 1912: [0.91839779 0.07109641 0.0105058 ]\n","Logit 1913: [0.6521301  0.32583673 0.02203316]\n","Logit 1914: [0.32497937 0.6661635  0.00885713]\n","Logit 1915: [0.0923783  0.89477093 0.01285077]\n","Logit 1916: [0.07446587 0.91095303 0.01458109]\n","Logit 1917: [0.91749719 0.07260461 0.0098982 ]\n","Logit 1918: [0.6521301  0.32583673 0.02203316]\n","Logit 1919: [0.09017248 0.02756827 0.88225925]\n","Logit 1920: [0.08607003 0.01398894 0.89994103]\n","Logit 1921: [0.76811973 0.2152872  0.01659306]\n","Logit 1922: [0.7868051  0.19122099 0.0219739 ]\n","Logit 1923: [0.02442469 0.96145319 0.01412213]\n","Logit 1924: [0.17184347 0.02488061 0.80327592]\n","Logit 1925: [0.09886588 0.01715098 0.88398314]\n","Logit 1926: [0.93652614 0.05166169 0.01181217]\n","Logit 1927: [0.60490858 0.02944772 0.3656437 ]\n","Logit 1928: [0.06047819 0.92785326 0.01166855]\n","Logit 1929: [0.2088015  0.77503444 0.01616406]\n","Logit 1930: [0.90517054 0.08267646 0.01215301]\n","Logit 1931: [0.94805601 0.03963089 0.0123131 ]\n","Logit 1932: [0.95228904 0.02572699 0.02198398]\n","Logit 1933: [0.93932761 0.0323625  0.02830989]\n","Logit 1934: [0.91041531 0.07190362 0.01768107]\n","Logit 1935: [0.95944797 0.03205433 0.0084977 ]\n","Logit 1936: [0.95059804 0.03331531 0.01608665]\n","Logit 1937: [0.94336048 0.04005561 0.01658391]\n","Logit 1938: [0.87613411 0.11495185 0.00891405]\n","Logit 1939: [0.20805546 0.75511282 0.03683172]\n","Logit 1940: [0.79002901 0.20072835 0.00924264]\n","Logit 1941: [0.80513702 0.18544359 0.00941939]\n","Logit 1942: [0.77823808 0.21213605 0.00962587]\n","Logit 1943: [0.79965565 0.19049213 0.00985222]\n","Logit 1944: [0.85824834 0.1330706  0.00868106]\n","Logit 1945: [0.85538943 0.13723885 0.00737172]\n","Logit 1946: [0.81206622 0.18004993 0.00788385]\n","Logit 1947: [0.09706966 0.80015373 0.10277661]\n","Logit 1948: [0.9262809  0.02105205 0.05266705]\n","Logit 1949: [0.51215376 0.03254961 0.45529662]\n","Logit 1950: [0.87067627 0.01619812 0.11312561]\n","Logit 1951: [0.94353415 0.03416744 0.02229841]\n","Logit 1952: [0.71881459 0.23536756 0.04581785]\n","Logit 1953: [0.06545184 0.92169292 0.01285524]\n","Logit 1954: [0.77928829 0.21138763 0.00932408]\n","Logit 1955: [0.54643009 0.03908392 0.41448599]\n","Logit 1956: [0.47907924 0.50207073 0.01885003]\n","Logit 1957: [0.07046543 0.9195655  0.00996907]\n","Logit 1958: [0.11678933 0.87047471 0.01273596]\n","Logit 1959: [0.70003943 0.28743592 0.01252466]\n","Logit 1960: [0.36709641 0.62141037 0.01149322]\n","Logit 1961: [0.93823531 0.04292816 0.01883652]\n","Logit 1962: [0.70590707 0.28563034 0.00846259]\n","Logit 1963: [0.42502108 0.03665153 0.53832739]\n","Logit 1964: [0.90162049 0.07152264 0.02685687]\n","Logit 1965: [0.67977343 0.29798779 0.02223878]\n","Logit 1966: [0.7199002  0.2711169  0.00898291]\n","Logit 1967: [0.68076353 0.30789474 0.01134173]\n","Logit 1968: [0.26096692 0.72160904 0.01742404]\n","Logit 1969: [0.7858965  0.20662129 0.00748221]\n","Logit 1970: [0.71032305 0.24291748 0.04675947]\n","Logit 1971: [0.78973463 0.03534835 0.17491703]\n","Logit 1972: [0.96072409 0.01257549 0.02670042]\n","Logit 1973: [0.9368936  0.05351815 0.00958825]\n","Logit 1974: [0.75012945 0.24175422 0.00811633]\n","Logit 1975: [0.42962096 0.03046014 0.5399189 ]\n","Logit 1976: [0.57683661 0.41305249 0.0101109 ]\n","Logit 1977: [0.59242265 0.39851133 0.00906602]\n","Logit 1978: [0.78372704 0.04438791 0.17188506]\n","Logit 1979: [0.17582258 0.02075183 0.80342559]\n","Logit 1980: [0.94723696 0.04353091 0.00923213]\n","Logit 1981: [0.53469523 0.45512136 0.0101834 ]\n","Logit 1982: [0.89900916 0.09088602 0.01010481]\n","Logit 1983: [0.91190253 0.07459811 0.01349936]\n","Logit 1984: [0.09732256 0.01943724 0.8832402 ]\n","Logit 1985: [0.04982949 0.93654789 0.01362262]\n","Logit 1986: [0.40597407 0.5770055  0.01702043]\n","Logit 1987: [0.53509308 0.44665061 0.01825631]\n","Logit 1988: [0.94506441 0.04176725 0.01316834]\n","Logit 1989: [0.87301436 0.10113279 0.02585285]\n","Logit 1990: [0.17259993 0.11738725 0.71001282]\n","Logit 1991: [0.95736394 0.03446571 0.00817035]\n","Logit 1992: [0.81078865 0.17880372 0.01040762]\n","Logit 1993: [0.94938179 0.03645379 0.01416442]\n","Logit 1994: [0.90892023 0.08249364 0.00858613]\n","Logit 1995: [0.87682866 0.11268021 0.01049113]\n","Logit 1996: [0.97162296 0.01883296 0.00954408]\n","Logit 1997: [0.71032305 0.24291748 0.04675947]\n","Logit 1998: [0.75921589 0.23019501 0.0105891 ]\n","Logit 1999: [0.91871699 0.07480832 0.00647469]\n","Logit 2000: [0.18624277 0.80035776 0.01339947]\n","Logit 2001: [0.14765853 0.83572603 0.01661544]\n","Logit 2002: [0.15332941 0.02380335 0.82286725]\n","Logit 2003: [0.9073712  0.08102681 0.01160199]\n","Logit 2004: [0.86289995 0.12854076 0.00855929]\n","Logit 2005: [0.76590581 0.203988   0.0301062 ]\n","Logit 2006: [0.36882351 0.61766315 0.01351334]\n","Logit 2007: [0.78558487 0.20613636 0.00827877]\n","Logit 2008: [0.03497934 0.95470096 0.0103197 ]\n","Logit 2009: [0.25909807 0.71889437 0.02200757]\n","Logit 2010: [0.21167701 0.76750692 0.02081608]\n","Logit 2011: [0.92706033 0.04757427 0.02536539]\n","Logit 2012: [0.90354679 0.0352058  0.06124742]\n","Logit 2013: [0.71285924 0.2093555  0.07778526]\n","Logit 2014: [0.42841123 0.5522439  0.01934487]\n","Logit 2015: [0.88271543 0.09109334 0.02619123]\n","Logit 2016: [0.71331118 0.02657255 0.26011627]\n","Logit 2017: [0.03259875 0.95081634 0.0165849 ]\n","Logit 2018: [0.17780984 0.78895153 0.03323863]\n","Logit 2019: [0.72444766 0.2663793  0.00917304]\n","Logit 2020: [0.17706407 0.81236453 0.0105714 ]\n","Logit 2021: [0.16361571 0.82726571 0.00911857]\n","Logit 2022: [0.87743946 0.11098513 0.01157541]\n","Logit 2023: [0.77884827 0.20617281 0.01497893]\n","Logit 2024: [0.103147   0.88422784 0.01262516]\n","Logit 2025: [0.87750969 0.112723   0.00976731]\n","Logit 2026: [0.17313967 0.79766323 0.0291971 ]\n","Logit 2027: [0.05603753 0.9299248  0.01403767]\n","Logit 2028: [0.61687877 0.3592955  0.02382573]\n","Logit 2029: [0.74332991 0.24752971 0.00914038]\n","Logit 2030: [0.02069473 0.96550614 0.01379913]\n","Logit 2031: [0.02025093 0.96531873 0.01443035]\n","Logit 2032: [0.11207239 0.87380388 0.01412372]\n","Logit 2033: [0.91232148 0.07671999 0.01095852]\n","Logit 2034: [0.5123656  0.03657592 0.45105849]\n","Logit 2035: [0.78301883 0.2009977  0.01598347]\n","Logit 2036: [0.22293488 0.76083672 0.0162284 ]\n","Logit 2037: [0.21703361 0.77170852 0.01125787]\n","Logit 2038: [0.03939398 0.01683593 0.94377009]\n","Logit 2039: [0.03747262 0.0189439  0.94358348]\n","Logit 2040: [0.06205823 0.81506543 0.12287635]\n","Logit 2041: [0.72893262 0.25475741 0.01630997]\n","Logit 2042: [0.02983625 0.95251224 0.01765152]\n","Logit 2043: [0.03411559 0.9448664  0.02101801]\n","Logit 2044: [0.7213871  0.04121784 0.23739506]\n","Logit 2045: [0.73151086 0.25640859 0.01208055]\n","Logit 2046: [0.38697444 0.59614716 0.0168784 ]\n","Logit 2047: [0.11402844 0.87591254 0.01005902]\n","Logit 2048: [0.90793939 0.0839402  0.00812041]\n","Logit 2049: [0.96580441 0.01791533 0.01628026]\n","Logit 2050: [0.20046056 0.77693278 0.02260666]\n","Logit 2051: [0.82228953 0.16808101 0.00962946]\n","Logit 2052: [0.44219181 0.04410477 0.51370342]\n","Logit 2053: [0.45343703 0.53584696 0.01071601]\n","Logit 2054: [0.77293025 0.218228   0.00884176]\n","Logit 2055: [0.89577287 0.09655249 0.00767464]\n","Logit 2056: [0.02182579 0.96644026 0.01173395]\n","Logit 2057: [0.71032305 0.24291748 0.04675947]\n","Logit 2058: [0.36180295 0.62391965 0.0142774 ]\n","Logit 2059: [0.93898768 0.02534288 0.03566945]\n","Logit 2060: [0.11989659 0.85437888 0.02572453]\n","Logit 2061: [0.91617036 0.07318428 0.01064536]\n","Logit 2062: [0.86165844 0.12846554 0.00987601]\n","Logit 2063: [0.86434671 0.12580434 0.00984895]\n","Logit 2064: [0.89360102 0.09886164 0.00753734]\n","Logit 2065: [0.71032305 0.24291748 0.04675947]\n","Logit 2066: [0.0257409  0.95653308 0.01772603]\n","Logit 2067: [0.92544416 0.06535822 0.00919763]\n","Logit 2068: [0.93427892 0.0586571  0.00706399]\n","Logit 2069: [0.91066587 0.07764444 0.01168969]\n","Logit 2070: [0.92255648 0.0656012  0.01184232]\n","Logit 2071: [0.95377864 0.0374545  0.00876686]\n","Logit 2072: [0.92702653 0.06407839 0.00889508]\n","Logit 2073: [0.04782513 0.94178387 0.010391  ]\n","Logit 2074: [0.29974354 0.68344488 0.01681158]\n","Logit 2075: [0.28654342 0.69616408 0.0172925 ]\n","Logit 2076: [0.83104795 0.15725876 0.0116933 ]\n","Logit 2077: [0.56439496 0.42147569 0.01412935]\n","Logit 2078: [0.02848901 0.96015669 0.0113543 ]\n","Logit 2079: [0.29349468 0.02210761 0.68439771]\n","Logit 2080: [0.63300432 0.01979904 0.34719664]\n","Logit 2081: [0.30910607 0.02523706 0.66565687]\n","Logit 2082: [0.40411742 0.58455221 0.01133037]\n","Logit 2083: [0.05411483 0.01800271 0.92788246]\n","Logit 2084: [0.8209519  0.16920632 0.00984178]\n","Logit 2085: [0.53761533 0.44613423 0.01625044]\n","Logit 2086: [0.07091851 0.91587612 0.01320537]\n","Logit 2087: [0.94808845 0.04323097 0.00868058]\n","Logit 2088: [0.0394919 0.9479656 0.0125425]\n","Logit 2089: [0.95616878 0.03336361 0.01046761]\n","Logit 2090: [0.49339751 0.03094688 0.4756556 ]\n","Logit 2091: [0.96125003 0.02487685 0.01387312]\n","Logit 2092: [0.94346816 0.04855833 0.00797351]\n","Logit 2093: [0.85770302 0.07151326 0.07078372]\n","Logit 2094: [0.94291741 0.05026646 0.00681613]\n","Logit 2095: [0.10739992 0.01750048 0.8750996 ]\n","Logit 2096: [0.91349843 0.07783842 0.00866315]\n","Logit 2097: [0.93917687 0.04104351 0.01977962]\n","Logit 2098: [0.93917687 0.04104351 0.01977962]\n","Logit 2099: [0.57955837 0.40737222 0.01306941]\n","Logit 2100: [0.48525192 0.49675934 0.01798875]\n","Logit 2101: [0.06783498 0.92332277 0.00884225]\n","Logit 2102: [0.93909885 0.04531648 0.01558467]\n","Logit 2103: [0.35145137 0.08788991 0.56065872]\n","Logit 2104: [0.12365566 0.01988783 0.8564565 ]\n","Logit 2105: [0.92848447 0.05702642 0.01448911]\n","Logit 2106: [0.93699536 0.02408395 0.03892069]\n","Logit 2107: [0.58378533 0.10735853 0.30885614]\n","Logit 2108: [0.49339751 0.03094688 0.4756556 ]\n","Logit 2109: [0.06783498 0.92332277 0.00884225]\n","Logit 2110: [0.52217659 0.46036942 0.01745399]\n","Logit 2111: [0.96814763 0.02085292 0.01099945]\n","Logit 2112: [0.20634512 0.02979222 0.76386266]\n","Logit 2113: [0.38016901 0.60988607 0.00994491]\n","Logit 2114: [0.90159847 0.03988519 0.05851634]\n","Logit 2115: [0.45666745 0.51873692 0.02459563]\n","Logit 2116: [0.72365646 0.26660859 0.00973496]\n","Logit 2117: [0.10238932 0.01998523 0.87762545]\n","Logit 2118: [0.10061145 0.8823526  0.01703596]\n","Logit 2119: [0.63559575 0.35480024 0.009604  ]\n","Logit 2120: [0.83378564 0.10392827 0.06228609]\n","Logit 2121: [0.51526055 0.43900748 0.04573197]\n","Logit 2122: [0.90572411 0.08590181 0.00837408]\n","Logit 2123: [0.35691664 0.62610241 0.01698095]\n","Logit 2124: [0.3274263 0.6562721 0.0163016]\n","Logit 2125: [0.90625959 0.08617059 0.00756982]\n","Logit 2126: [0.0373888  0.01883703 0.94377417]\n","Logit 2127: [0.1299686  0.01848529 0.8515461 ]\n","Logit 2128: [0.16754089 0.01909125 0.81336786]\n","Logit 2129: [0.80191198 0.18788442 0.0102036 ]\n","Logit 2130: [0.35650066 0.63243553 0.01106381]\n","Logit 2131: [0.9441938  0.03544935 0.02035685]\n","Logit 2132: [0.50038286 0.48783776 0.01177937]\n","Logit 2133: [0.95130855 0.03594391 0.01274754]\n","Logit 2134: [0.07571004 0.02111651 0.90317345]\n","Logit 2135: [0.12700652 0.01945569 0.85353778]\n","Logit 2136: [0.74538191 0.05253885 0.20207924]\n","Logit 2137: [0.74289993 0.06303969 0.19406039]\n","Logit 2138: [0.36107102 0.62448432 0.01444466]\n","Logit 2139: [0.06102263 0.92642914 0.01254823]\n","Logit 2140: [0.90364702 0.08744188 0.0089111 ]\n","Logit 2141: [0.05404447 0.91856713 0.0273884 ]\n","Logit 2142: [0.82657215 0.16292073 0.01050712]\n","Logit 2143: [0.54849711 0.43815496 0.01334794]\n","Logit 2144: [0.23367891 0.75616862 0.01015247]\n","Logit 2145: [0.96790878 0.02088853 0.01120268]\n","Logit 2146: [0.03531108 0.9534578  0.01123112]\n","Logit 2147: [0.8282451  0.16340998 0.00834492]\n","Logit 2148: [0.19451514 0.79180773 0.01367713]\n","Logit 2149: [0.06038955 0.91670595 0.02290451]\n","Logit 2150: [0.07367366 0.90082185 0.02550449]\n","Logit 2151: [0.20987686 0.77636785 0.01375529]\n","Logit 2152: [0.60177392 0.38701969 0.01120638]\n","Logit 2153: [0.08180249 0.90524697 0.01295054]\n","Logit 2154: [0.87288383 0.11671295 0.01040322]\n","Logit 2155: [0.13071454 0.85611988 0.01316558]\n","Logit 2156: [0.90320225 0.08347931 0.01331844]\n","Logit 2157: [0.08180249 0.90524697 0.01295054]\n","Logit 2158: [0.01957982 0.96672454 0.01369564]\n","Logit 2159: [0.05100216 0.93867148 0.01032636]\n","Logit 2160: [0.0289886  0.95809744 0.01291396]\n","Logit 2161: [0.89244926 0.08904662 0.01850411]\n","Logit 2162: [0.13890977 0.0191538  0.84193642]\n","Logit 2163: [0.93179663 0.05828741 0.00991596]\n","Logit 2164: [0.55487545 0.42793788 0.01718667]\n","Logit 2165: [0.29475661 0.69510306 0.01014034]\n","Logit 2166: [0.46194901 0.52859373 0.00945726]\n","Logit 2167: [0.76464317 0.22459789 0.01075894]\n","Logit 2168: [0.16492495 0.82457286 0.01050219]\n","Logit 2169: [0.05788028 0.9302713  0.01184843]\n","Logit 2170: [0.86991942 0.12248044 0.00760014]\n","Logit 2171: [0.90419103 0.08757867 0.0082303 ]\n","Logit 2172: [0.09537055 0.89354494 0.01108451]\n","Logit 2173: [0.22652148 0.76257964 0.01089888]\n","Logit 2174: [0.85710698 0.13544857 0.00744445]\n","Logit 2175: [0.04614016 0.94134399 0.01251585]\n","Logit 2176: [0.10146805 0.01855103 0.87998092]\n","Logit 2177: [0.07567685 0.02044288 0.90388027]\n","Logit 2178: [0.64626383 0.34055711 0.01317907]\n","Logit 2179: [0.21777793 0.10002295 0.68219912]\n","Logit 2180: [0.62664861 0.35985402 0.01349738]\n","Logit 2181: [0.82672426 0.16307011 0.01020563]\n","Logit 2182: [0.71345426 0.27119366 0.01535208]\n","Logit 2183: [0.03384303 0.95254311 0.01361386]\n","Logit 2184: [0.9596036  0.02605149 0.01434491]\n","Logit 2185: [0.91368016 0.04069692 0.04562292]\n","Logit 2186: [0.91935773 0.06867856 0.01196372]\n","Logit 2187: [0.76353308 0.06146238 0.17500454]\n","Logit 2188: [0.73998136 0.25041734 0.0096013 ]\n","Logit 2189: [0.81541555 0.17587303 0.00871142]\n","Logit 2190: [0.92311296 0.06624448 0.01064256]\n","Logit 2191: [0.78214585 0.03000305 0.1878511 ]\n","Logit 2192: [0.6918794  0.29976112 0.00835948]\n","Logit 2193: [0.95034214 0.0380742  0.01158366]\n","Logit 2194: [0.63302473 0.35613653 0.01083874]\n","Logit 2195: [0.94736263 0.04581581 0.00682156]\n","Logit 2196: [0.92098903 0.06809855 0.01091242]\n","Logit 2197: [0.37441843 0.61550574 0.01007583]\n","Logit 2198: [0.39237296 0.59888187 0.00874517]\n","Logit 2199: [0.17122152 0.81804961 0.01072887]\n","Logit 2200: [0.04561694 0.93911448 0.01526857]\n","Logit 2201: [0.53625367 0.45289577 0.01085056]\n","Logit 2202: [0.0780156  0.01972062 0.90226378]\n","Logit 2203: [0.91358517 0.07926015 0.00715468]\n","Logit 2204: [0.30002347 0.6897852  0.01019132]\n","Logit 2205: [0.71957886 0.16695572 0.11346542]\n","Logit 2206: [0.46557904 0.52500043 0.00942053]\n","Logit 2207: [0.89196    0.09951477 0.00852524]\n","Logit 2208: [0.37122155 0.61925542 0.00952303]\n","Logit 2209: [0.77391617 0.21680593 0.0092779 ]\n","Logit 2210: [0.56501831 0.42525052 0.00973118]\n","Logit 2211: [0.89869154 0.09393265 0.00737581]\n","Logit 2212: [0.39951472 0.59044448 0.0100408 ]\n","Logit 2213: [0.96993805 0.02269965 0.0073623 ]\n","Logit 2214: [0.69635983 0.2945695  0.00907067]\n","Logit 2215: [0.85024873 0.14164056 0.00811071]\n","Logit 2216: [0.57933191 0.39314568 0.02752241]\n","Logit 2217: [0.04782302 0.93190765 0.02026933]\n","Logit 2218: [0.67196862 0.31881346 0.00921792]\n","Logit 2219: [0.70314275 0.28772502 0.00913223]\n","Logit 2220: [0.02110257 0.96454883 0.0143486 ]\n","Logit 2221: [0.27199807 0.71907305 0.00892888]\n","Logit 2222: [0.82757342 0.1363485  0.03607807]\n","Logit 2223: [0.87226631 0.11658056 0.01115313]\n","Logit 2224: [0.07465275 0.91309975 0.0122475 ]\n","Logit 2225: [0.14402794 0.84352151 0.01245055]\n","Logit 2226: [0.3794269  0.61048144 0.01009165]\n","Logit 2227: [0.82527525 0.15045211 0.02427264]\n","Logit 2228: [0.08666704 0.89729481 0.01603815]\n","Logit 2229: [0.58141382 0.40708314 0.01150304]\n","Logit 2230: [0.83713703 0.15053076 0.01233221]\n","Logit 2231: [0.12073667 0.8677485  0.01151484]\n","Logit 2232: [0.89094362 0.09890437 0.01015201]\n","Logit 2233: [0.57933191 0.39314568 0.02752241]\n","Logit 2234: [0.08625134 0.89981619 0.01393247]\n","Logit 2235: [0.08487999 0.90417417 0.01094584]\n","Logit 2236: [0.956691   0.02463827 0.01867073]\n","Logit 2237: [0.71189096 0.27634102 0.01176802]\n","Logit 2238: [0.43364637 0.55086358 0.01549005]\n","Logit 2239: [0.12391482 0.8637167  0.01236848]\n","Logit 2240: [0.85525951 0.13588451 0.00885598]\n","Logit 2241: [0.33899835 0.65152485 0.0094768 ]\n","Logit 2242: [0.14366639 0.84572896 0.01060465]\n","Logit 2243: [0.87790024 0.11519763 0.00690213]\n","Logit 2244: [0.84654247 0.14610484 0.00735269]\n","Logit 2245: [0.86351076 0.1184975  0.01799174]\n","Logit 2246: [0.91179508 0.06195761 0.02624732]\n","Logit 2247: [0.81780406 0.1468212  0.03537473]\n","Logit 2248: [0.36539213 0.06354215 0.57106572]\n","Logit 2249: [0.57601919 0.0428834  0.38109742]\n","Logit 2250: [0.51269415 0.04662881 0.44067704]\n","Logit 2251: [0.73681648 0.25007789 0.01310563]\n","Logit 2252: [0.84711001 0.03137251 0.12151748]\n","Logit 2253: [0.83644186 0.15472619 0.00883195]\n","Logit 2254: [0.79586303 0.1927624  0.01137457]\n","Logit 2255: [0.91288234 0.07851093 0.00860673]\n","Logit 2256: [0.80104553 0.18896879 0.00998568]\n","Logit 2257: [0.9106615 0.0472836 0.0420549]\n","Logit 2258: [0.82007223 0.14462771 0.03530006]\n","Logit 2259: [0.03722228 0.02091041 0.94186731]\n","Logit 2260: [0.64394616 0.3373533  0.01870054]\n","Logit 2261: [0.06941359 0.74408331 0.1865031 ]\n","Logit 2262: [0.03858281 0.01884972 0.94256747]\n","Logit 2263: [0.90769203 0.08321602 0.00909195]\n","Logit 2264: [0.1851011  0.80129901 0.0135999 ]\n","Logit 2265: [0.5765441  0.40604577 0.01741012]\n","Logit 2266: [0.9091763  0.08068413 0.01013957]\n","Logit 2267: [0.46638479 0.50823733 0.02537789]\n","Logit 2268: [0.96847789 0.02137558 0.01014653]\n","Logit 2269: [0.9082606  0.03276188 0.05897752]\n","Logit 2270: [0.96038357 0.02524583 0.0143706 ]\n","Logit 2271: [0.03714079 0.94902807 0.01383114]\n","Logit 2272: [0.86822741 0.02192753 0.10984506]\n","Logit 2273: [0.96597187 0.02480451 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0.00958281]\n","Logit 2547: [0.09888772 0.01736546 0.88374682]\n","Logit 2548: [0.95759047 0.03233779 0.01007173]\n","Logit 2549: [0.95161146 0.03868764 0.00970091]\n","Logit 2550: [0.60501813 0.38344789 0.01153398]\n","Logit 2551: [0.67949495 0.31124739 0.00925766]\n","Logit 2552: [0.90728208 0.08198889 0.01072903]\n","Logit 2553: [0.85892225 0.13428515 0.00679261]\n","Logit 2554: [0.7091377  0.27851709 0.01234521]\n","Logit 2555: [0.87219591 0.11803888 0.00976521]\n","Logit 2556: [0.95725354 0.03090639 0.01184008]\n","Logit 2557: [0.84934473 0.06104752 0.08960774]\n","Logit 2558: [0.96313616 0.02579234 0.0110715 ]\n","Logit 2559: [0.65178102 0.33616404 0.01205494]\n","Logit 2560: [0.02097899 0.96171354 0.01730748]\n","Logit 2561: [0.75471567 0.05613212 0.18915221]\n","Logit 2562: [0.71715047 0.03789639 0.24495314]\n","Logit 2563: [0.07629664 0.01597313 0.90773023]\n","Logit 2564: [0.95491498 0.02773125 0.01735377]\n","Logit 2565: [0.94628307 0.0416174  0.01209953]\n","Logit 2566: 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0.01145864]\n","Logit 2586: [0.18389366 0.80506267 0.01104367]\n","Logit 2587: [0.95141543 0.03990749 0.00867708]\n","Logit 2588: [0.94439634 0.04262331 0.01298035]\n","Logit 2589: [0.89016201 0.10288034 0.00695764]\n","Logit 2590: [0.52673937 0.46484587 0.00841476]\n","Logit 2591: [0.88236274 0.10779673 0.00984053]\n","Logit 2592: [0.14034696 0.82689457 0.03275847]\n","Logit 2593: [0.53099679 0.0746888  0.3943144 ]\n","Logit 2594: [0.03329858 0.95290834 0.01379308]\n","Logit 2595: [0.05288096 0.93368319 0.01343585]\n","Logit 2596: [0.96296783 0.02494566 0.01208651]\n","Logit 2597: [0.02124615 0.96685329 0.01190056]\n","Logit 2598: [0.20459229 0.7777992  0.01760851]\n","Logit 2599: [0.0445604  0.94279381 0.01264579]\n","Logit 2600: [0.04408366 0.94071116 0.01520518]\n","Logit 2601: [0.23048809 0.76018253 0.00932937]\n","Logit 2602: [0.1209171  0.86809103 0.01099187]\n","Logit 2603: [0.0468284  0.94103298 0.01213862]\n","Logit 2604: [0.96398973 0.02311801 0.01289226]\n","Logit 2605: 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0.01123317]\n","Logit 2625: [0.87177807 0.11998666 0.00823526]\n","Logit 2626: [0.83470998 0.15603532 0.0092547 ]\n","Logit 2627: [0.10085655 0.89051486 0.00862859]\n","Logit 2628: [0.64197192 0.3493751  0.00865298]\n","Logit 2629: [0.73617024 0.25329805 0.01053171]\n","Logit 2630: [0.80621237 0.18285703 0.0109306 ]\n","Logit 2631: [0.3306736  0.65666036 0.01266604]\n","Logit 2632: [0.08235068 0.90028412 0.0173652 ]\n","Logit 2633: [0.91741262 0.0741383  0.00844908]\n","Logit 2634: [0.79770701 0.19067855 0.01161444]\n","Logit 2635: [0.73310904 0.25621705 0.01067391]\n","Logit 2636: [0.74937667 0.24198377 0.00863956]\n","Logit 2637: [0.55990029 0.42719941 0.0129003 ]\n","Logit 2638: [0.94669687 0.04414807 0.00915506]\n","Logit 2639: [0.91937418 0.07138081 0.00924501]\n","Logit 2640: [0.95337752 0.03743875 0.00918372]\n","Logit 2641: [0.76103783 0.22895173 0.01001044]\n","Logit 2642: [0.95668765 0.03037956 0.01293279]\n","Logit 2643: [0.88105652 0.07151386 0.04742961]\n","Logit 2644: 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0.02168462]\n","Logit 2664: [0.26970946 0.716163   0.01412755]\n","Logit 2665: [0.02596457 0.95827173 0.01576369]\n","Logit 2666: [0.02947902 0.95313182 0.01738916]\n","Logit 2667: [0.89424833 0.09333997 0.0124117 ]\n","Logit 2668: [0.9104616  0.07266614 0.01687225]\n","Logit 2669: [0.16332221 0.82236149 0.0143163 ]\n","Logit 2670: [0.77672131 0.21421832 0.00906037]\n","Logit 2671: [0.95945795 0.02795858 0.01258347]\n","Logit 2672: [0.02354751 0.96384842 0.01260407]\n","Logit 2673: [0.02327873 0.96549186 0.01122941]\n","Logit 2674: [0.02491248 0.96403806 0.01104945]\n","Logit 2675: [0.88309532 0.10574464 0.01116004]\n","Logit 2676: [0.09735521 0.87869642 0.02394837]\n","Logit 2677: [0.55977278 0.43129347 0.00893374]\n","Logit 2678: [0.32263083 0.65396361 0.02340556]\n","Logit 2679: [0.0648009  0.91397561 0.02122349]\n","Logit 2680: [0.64243168 0.34877278 0.00879554]\n","Logit 2681: [0.58683956 0.39321669 0.01994374]\n","Logit 2682: [0.67420631 0.31185554 0.01393815]\n","Logit 2683: 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0.00851908]\n","Logit 2820: [0.03370787 0.95431423 0.0119779 ]\n","Logit 2821: [0.04641965 0.94312364 0.01045671]\n","Logit 2822: [0.11802408 0.87400484 0.00797108]\n","Logit 2823: [0.35614603 0.63427922 0.00957475]\n","Logit 2824: [0.69437817 0.29574604 0.0098758 ]\n","Logit 2825: [0.08258046 0.89883749 0.01858205]\n","Logit 2826: [0.14576887 0.84562747 0.00860367]\n","Logit 2827: [0.46334835 0.03442799 0.50222366]\n","Logit 2828: [0.05700775 0.92886067 0.01413158]\n","Logit 2829: [0.95737719 0.02821301 0.0144098 ]\n","Logit 2830: [0.86963158 0.12252963 0.0078388 ]\n","Logit 2831: [0.5957796  0.39571376 0.00850664]\n","Logit 2832: [0.9099969  0.07870347 0.01129962]\n","Logit 2833: [0.04999818 0.9365121  0.01348972]\n","Logit 2834: [0.10165501 0.86349586 0.03484913]\n","Logit 2835: [0.32920688 0.0379321  0.63286102]\n","Logit 2836: [0.24188865 0.74144032 0.01667104]\n","Logit 2837: [0.25172014 0.73732218 0.01095768]\n","Logit 2838: [0.70643108 0.28320277 0.01036615]\n","Logit 2839: [0.5768195  0.41304024 0.01014026]\n","Logit 2840: [0.62526446 0.3636466  0.01108894]\n","Logit 2841: [0.12634456 0.83033172 0.04332372]\n","Logit 2842: [0.03114489 0.95386779 0.01498732]\n","Logit 2843: [0.93508998 0.05068367 0.01422635]\n","Logit 2844: [0.54107296 0.43222433 0.02670271]\n","Logit 2845: [0.09756812 0.88936856 0.01306332]\n","Logit 2846: [0.14591343 0.82898009 0.02510648]\n","Logit 2847: [0.40077138 0.43503322 0.1641954 ]\n","Logit 2848: [0.08987093 0.8854485  0.02468057]\n","Logit 2849: [0.0681478  0.91907103 0.01278117]\n","Logit 2850: [0.95436961 0.0362007  0.00942969]\n","Logit 2851: [0.94241196 0.04852766 0.00906038]\n","Logit 2852: [0.01734467 0.97228824 0.01036709]\n","Logit 2853: [0.95436961 0.0362007  0.00942969]\n","Logit 2854: [0.03112797 0.95895204 0.00991999]\n","Logit 2855: [0.33760869 0.64411906 0.01827225]\n","Logit 2856: [0.74032798 0.25077943 0.00889259]\n","Logit 2857: [0.66871796 0.32273991 0.00854213]\n","Logit 2858: [0.92017325 0.07062776 0.00919899]\n","Logit 2859: [0.05907336 0.91687165 0.02405499]\n","Logit 2860: [0.87055266 0.11122009 0.01822724]\n","Logit 2861: [0.16349026 0.82200269 0.01450704]\n","Logit 2862: [0.80429806 0.17009907 0.02560287]\n","Logit 2863: [0.39270496 0.59763516 0.00965987]\n","Logit 2864: [0.94042011 0.05172473 0.00785516]\n","Logit 2865: [0.30118278 0.6884051  0.01041213]\n","Logit 2866: [0.06788659 0.91969286 0.01242055]\n","Logit 2867: [0.09505002 0.88506893 0.01988105]\n","Logit 2868: [0.32612336 0.65334147 0.02053517]\n","Logit 2869: [0.94965593 0.01921534 0.03112872]\n","Logit 2870: [0.38579657 0.60339729 0.01080614]\n","Logit 2871: [0.93093549 0.05512095 0.01394356]\n","Logit 2872: [0.91440968 0.0687439  0.01684642]\n","Logit 2873: [0.79036454 0.02825978 0.18137568]\n","Logit 2874: [0.96223321 0.02581854 0.01194825]\n","Logit 2875: [0.95826729 0.03217159 0.00956112]\n","Logit 2876: [0.34993574 0.02866843 0.62139583]\n","Logit 2877: [0.91369268 0.07752305 0.00878427]\n","Logit 2878: [0.94351953 0.04620165 0.01027882]\n","Logit 2879: [0.92971417 0.05868487 0.01160096]\n","Logit 2880: [0.2475131  0.73929854 0.01318836]\n","Logit 2881: [0.77222614 0.21569962 0.01207424]\n","Logit 2882: [0.7802694  0.02961127 0.19011933]\n","Logit 2883: [0.93734925 0.04612417 0.01652658]\n","Logit 2884: [0.63404236 0.34004191 0.02591573]\n","Logit 2885: [0.75634834 0.22010991 0.02354175]\n","Logit 2886: [0.04513403 0.91320527 0.0416607 ]\n","Logit 2887: [0.774855   0.21318249 0.01196251]\n","Logit 2888: [0.04626128 0.88966791 0.06407081]\n","Logit 2889: [0.80191076 0.18834339 0.00974585]\n","Logit 2890: [0.93916408 0.05286772 0.0079682 ]\n","Logit 2891: [0.3630471  0.61156237 0.02539053]\n","Logit 2892: [0.27413968 0.70587525 0.01998507]\n","Logit 2893: [0.79673849 0.19354063 0.00972088]\n","Logit 2894: [0.92917995 0.06104713 0.00977292]\n","Logit 2895: [0.96538858 0.02469288 0.00991854]\n","Logit 2896: [0.13622216 0.855294   0.00848385]\n","Logit 2897: [0.56128037 0.35590155 0.08281808]\n","Logit 2898: [0.83609189 0.15451048 0.00939763]\n","Logit 2899: [0.42753024 0.0447981  0.52767166]\n","Logit 2900: [0.71891891 0.19065739 0.0904237 ]\n","Logit 2901: [0.69623553 0.28924383 0.01452064]\n","Logit 2902: [0.96624765 0.02252512 0.01122723]\n","Logit 2903: [0.52895688 0.46136439 0.00967873]\n","Logit 2904: [0.28982571 0.69322184 0.01695245]\n","Logit 2905: [0.23880262 0.74714728 0.0140501 ]\n","Logit 2906: [0.22206947 0.76308183 0.0148487 ]\n","Logit 2907: [0.08558502 0.02477324 0.88964174]\n","Logit 2908: [0.30164608 0.68343087 0.01492305]\n","Logit 2909: [0.18824426 0.78944813 0.02230761]\n","Logit 2910: [0.70428978 0.2834494  0.01226082]\n","Logit 2911: [0.9582035  0.03202839 0.00976811]\n","Logit 2912: [0.34645686 0.15325213 0.50029101]\n","Logit 2913: [0.96093993 0.0266283  0.01243177]\n","Logit 2914: [0.92849511 0.0273085  0.0441964 ]\n","Logit 2915: [0.55446782 0.03388888 0.4116433 ]\n","Logit 2916: [0.51591874 0.47112759 0.01295367]\n","Logit 2917: [0.19815927 0.78949645 0.01234427]\n","Logit 2918: [0.94393805 0.04721441 0.00884753]\n","Logit 2919: [0.81658036 0.17356312 0.00985652]\n","Logit 2920: [0.09483254 0.88991531 0.01525214]\n","Logit 2921: [0.91110581 0.07987436 0.00901982]\n","Logit 2922: [0.28299679 0.68855536 0.02844784]\n","Logit 2923: [0.49634404 0.48437299 0.01928297]\n","Logit 2924: [0.04054269 0.94462963 0.01482769]\n","Logit 2925: [0.03403703 0.95333708 0.01262589]\n","Logit 2926: [0.87260287 0.11264478 0.01475235]\n","Logit 2927: [0.23022449 0.01912541 0.7506501 ]\n","Logit 2928: [0.93049859 0.05887795 0.01062346]\n","Logit 2929: [0.12670459 0.85902565 0.01426976]\n","Logit 2930: [0.0694224  0.91860037 0.01197723]\n","Logit 2931: [0.30932788 0.02599966 0.66467246]\n","Logit 2932: [0.70893475 0.27198738 0.01907787]\n","Logit 2933: [0.872267   0.11406789 0.01366511]\n","Logit 2934: [0.94664531 0.04456801 0.00878668]\n","Logit 2935: [0.83201387 0.05251789 0.11546825]\n","Logit 2936: [0.6248502 0.3644719 0.0106779]\n","Logit 2937: [0.88606098 0.09842315 0.01551588]\n","Logit 2938: [0.83396512 0.15581998 0.0102149 ]\n","Logit 2939: [0.96478921 0.02293472 0.01227607]\n","Logit 2940: [0.12935574 0.806452   0.06419226]\n","Logit 2941: [0.10810405 0.8753702  0.01652576]\n","Logit 2942: [0.64875393 0.3378864  0.01335968]\n","Logit 2943: [0.93600809 0.04394902 0.02004288]\n","Logit 2944: [0.71952558 0.26886707 0.01160736]\n","Logit 2945: [0.12670459 0.85902565 0.01426976]\n","Logit 2946: [0.0626876  0.92513164 0.01218076]\n","Logit 2947: [0.08249372 0.90501848 0.0124878 ]\n","Logit 2948: [0.75788271 0.23012764 0.01198966]\n","Logit 2949: [0.39778441 0.59076481 0.01145079]\n","Logit 2950: [0.55640856 0.43164202 0.01194942]\n","Logit 2951: [0.59882225 0.39195094 0.00922681]\n","Logit 2952: [0.89447418 0.09665334 0.00887248]\n","Logit 2953: [0.78368495 0.20513639 0.01117867]\n","Logit 2954: [0.05699364 0.93488635 0.00812001]\n","Logit 2955: [0.04651363 0.92336681 0.03011957]\n","Logit 2956: [0.96411335 0.02144614 0.0144405 ]\n","Logit 2957: [0.90249377 0.08888032 0.00862592]\n","Logit 2958: [0.26808769 0.72024783 0.01166448]\n","Logit 2959: [0.50715407 0.47318144 0.0196645 ]\n","Logit 2960: [0.33243281 0.64801785 0.01954934]\n","Logit 2961: [0.1530671  0.78496729 0.06196561]\n","Logit 2962: [0.1296865  0.85793151 0.01238199]\n","Logit 2963: [0.09401546 0.8933287  0.01265584]\n","Logit 2964: [0.79819986 0.13940229 0.06239785]\n","Logit 2965: [0.5300215  0.45869538 0.01128312]\n","Logit 2966: [0.95726985 0.0332718  0.00945835]\n","Logit 2967: [0.05328465 0.01686529 0.92985006]\n"]}]},{"cell_type":"code","source":["import pandas as pd\n","\n","# 1. 기존 데이터프레임(df)와 감성 분석 결과(preds1, logit1)를 준비했다고 가정합니다.\n","# df['news_title']: 기사 제목이 담긴 데이터프레임의 칼럼\n","# preds1: 감성 분석 결과 (라벨, 예: 'Positive', 'Neutral', 'Negative')\n","# logit1: 각 감성에 대한 확률 (2D 리스트 또는 배열 형태)\n","\n","# 예제 데이터 (실제 데이터로 대체하세요)\n","# df = pd.read_csv(\"input.csv\")\n","# preds1 = ['Positive', 'Negative', 'Neutral']  # 감성 라벨\n","# logit1 = [[0.8, 0.1, 0.1], [0.2, 0.3, 0.5], [0.4, 0.4, 0.2]]  # 확률\n","\n","# 2. 새로운 데이터프레임 생성\n","output_df = pd.DataFrame({\n","    'Article Title': df['kor_sentence'],  # 기사 제목\n","    'Date' : df[\"Date\"],\n","    'Sentiment Label': preds1,  # 감성 라벨\n","    'Neutral Probability': [x[0] for x in new_logit1],  # 긍정 확률\n","    'Positive Probability': [x[1] for x in new_logit1],   # 중립 확률\n","    'Negative Probability': [x[2] for x in new_logit1]  # 부정 확률\n","})\n","\n","# 3. 새로운 파일로 저장\n","output_file = \"삼전_sentiment_results.csv\"  # 출력 파일 이름\n","output_df.to_csv(output_file, index=False, encoding='utf-8-sig')  # UTF-8로 저장 (한글 호환)\n","\n","print(f\"결과가 {output_file}에 저장되었습니다!\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"glUaNzXbkaq6","executionInfo":{"status":"ok","timestamp":1731905613715,"user_tz":-540,"elapsed":529,"user":{"displayName":"‍임주현[학생](공과대학 산업경영공학과)","userId":"14719868373122672648"}},"outputId":"3481ecc4-2ecc-415e-be9c-ed00b123838d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["결과가 삼전_sentiment_results.csv에 저장되었습니다!\n"]}]},{"cell_type":"code","source":[],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"P2XgRsx9dExI","executionInfo":{"status":"ok","timestamp":1731904522459,"user_tz":-540,"elapsed":5,"user":{"displayName":"‍임주현[학생](공과대학 산업경영공학과)","userId":"14719868373122672648"}},"outputId":"db0c85d9-d639-4e1a-db14-41051f27bdc6"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["numpy.ndarray"]},"metadata":{},"execution_count":70}]},{"cell_type":"code","source":[],"metadata":{"id":"U6hfPXfGd1qd"},"execution_count":null,"outputs":[]}]}