src/qald/finetune-qald-t5-base.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "fTZYaL1ANwxu"
      },
      "source": [
        "# Following https://github.com/huggingface/notebooks/blob/master/examples/translation.ipynb"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "566GHCfaILnG"
      },
      "source": [
        "!pip install wandb datasets transformers sacrebleu==1.5.1  bert-score -qqq"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EXgnU7gQOGGS",
        "outputId": "8361c4d4-de91-4236-8001-fa2f6dad4335"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tue Oct 19 23:02:07 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 470.74       Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   33C    P8    28W / 149W |      0MiB / 11441MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q9TjriZbNwxy",
        "outputId": "3c8616d2-ba47-4b69-8aa2-bb683f06fc9a"
      },
      "source": [
        "import datetime\n",
        "from tqdm import tqdm\n",
        "\n",
        "model_checkpoint = 't5-base'\n",
        "fp16 = True\n",
        "todaydate = datetime.datetime.now().strftime(\"%Y-%m-%d_%H-%M\")\n",
        "gdir = f'drive/My Drive/Colab Notebooks/{todaydate}/'\n",
        "model_name=f'sparql-qald9-{model_checkpoint}-{todaydate}'\n",
        "model_path='./models/'+model_name\n",
        "ds_path= 'qald-text-to-sparql'\n",
        "print(model_name)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sparql-qald9-t5-base-2021-10-19_23-02\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "id": "ueKQ2exH7rX-",
        "outputId": "4939a634-22c8-4ce4-d700-5e7c9489af26"
      },
      "source": [
        "# Flexible integration for any Python script\n",
        "import wandb\n",
        "\n",
        "# 1. Start a W&B run\n",
        "wandb.init(project='text-to-sparql', entity='shahriar', name= model_name)\n",
        "\n",
        "# 2. Save model inputs and hyperparameters\n",
        "config = wandb.config\n",
        "config.learning_rate = 0.01"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mshahriar\u001b[0m (use `wandb login --relogin` to force relogin)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/shahriar/text-to-sparql/runs/1ew1fb14\" target=\"_blank\">sparql-qald9-t5-base-2021-10-19_23-02</a></strong> to <a href=\"https://wandb.ai/shahriar/text-to-sparql\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "\n",
              "                "
            ],
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              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Wpgv7Jit9qN5",
        "outputId": "8625a9b1-6b18-4b3c-f8a7-a3f3a85f4a07"
      },
      "source": [
        "model_name"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'sparql-qald9-t5-base-2021-10-19_23-02'"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Acx8YqC8Ok-H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "13d17312-53fc-4a9c-e50f-323afb786103"
      },
      "source": [
        "!mkdir models"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mkdir: cannot create directory ‘models’: File exists\n"
          ]
        }
      ]
    },
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        "id": "Xcur-Q7hRL2u",
        "outputId": "afe35069-b023-4774-b887-1d45b879d8e6"
      },
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Login successful\n",
            "Your token has been saved to /root/.huggingface/token\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "so9FhoYARRRg"
      },
      "source": [
        "!apt install git-lfs -qqq\n",
        "!git config --global credential.helper store"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "id": "F41jBFXsgyJQ",
        "outputId": "17f01747-7bf2-4648-e5e6-ea4beae13a5b"
      },
      "source": [
        "!unzip qald-text-to-sparql.zip"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  qald-text-to-sparql.zip\n",
            "replace qald-text-to-sparql/dataset_dict.json? [y]es, [n]o, [A]ll, [N]one, [r]ename: nb\n",
            "replace qald-text-to-sparql/test.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: "
          ]
        }
      ]
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            "dc9df8a6db2f4465a86faa31d0f7c87f",
            "63d96cafbb854a3e87291e66b6f467b1",
            "1d3fb2ee94bf4393904140e3883ad853",
            "18db92b424d64f8ea0db23399238034c"
          ]
        },
        "id": "iBRNagL8LNVj",
        "outputId": "6c7ff08e-48b1-41d6-84a7-2149119c7562"
      },
      "source": [
        "x = 'yazdipour/text-to-sparql-t5-base-2021-10-19_15-35_lastDS'\n",
        "from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM, T5TokenizerFast\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(x).to(\"cuda\")\n",
        "tokenizer = AutoTokenizer.from_pretrained(x)\n",
        "# tokenizer = T5TokenizerFast.from_pretrained(model_name)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "24eaf57feefc4d31bade4a142e725932",
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "62fc1732c2d84276aac5a790582e6c33",
              "version_minor": 0,
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            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2ffc865ef524418b92dd9256c0242f9c",
              "version_minor": 0,
              "version_major": 2
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c308b804cc5444b2975e5466e4151660",
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "52b0cdea118d4464a566b7abeeb3fd59",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I0gq9_ijtbHt"
      },
      "source": [
        "from datasets import load_dataset, load_metric, Dataset, load_from_disk\n",
        "raw_datasets = load_from_disk(ds_path)\n",
        "# raw_datasets = load_dataset(\"yazdipour/text-to-sparql-t5-lc-quad-v2\", data_files={\"train\": \"train.csv\", \"test\": \"test.csv\"})"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HGDdBpGfNwxz"
      },
      "source": [
        "# Preprocessing"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EZdjRilTNwx1",
        "outputId": "2437e203-0ed1-4c61-bff8-19fe926fd243"
      },
      "source": [
        "print(raw_datasets['test']['translation'][0]['sparql'],'\\n',\n",
        "      raw_datasets['test']['translation'][0]['en'],'\\n',\n",
        "      tokenizer(raw_datasets['test']['translation'][0]['en']))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "select distinct ?uri where [ res:Salt_Lake_City onto:timeZone ?uri ] \n",
            " what is the time zone of salt lake city? \n",
            " {'input_ids': [125, 19, 8, 97, 2901, 13, 3136, 6957, 690, 58, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kkidcZ_hNwx2",
        "outputId": "b134a247-76d0-41d9-8dba-3a9aff13898b"
      },
      "source": [
        "max_input_length = 0 \n",
        "max_target_length = 0\n",
        "for d in tqdm(raw_datasets['train']['translation']):\n",
        "    len_en = len(d['en'])\n",
        "    len_qry = len(d['sparql'])\n",
        "    if len_en > max_input_length: max_input_length=len_en\n",
        "    if len_qry > max_target_length: max_target_length=len_qry\n",
        "print('\\n',max_input_length, max_target_length)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 408/408 [00:00<00:00, 120972.43it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " 85 299\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OXiURhR-Nwx3"
      },
      "source": [
        "source_lang = \"en\"\n",
        "target_lang = \"sparql\"\n",
        "prefix = \"translate english to sparql2: \"\n",
        "\n",
        "def preprocess_function(examples):\n",
        "    inputs = []\n",
        "    targets= []\n",
        "    for ex in examples[\"translation\"]:\n",
        "      inputs.append(prefix + ex[source_lang])\n",
        "      targets.append(ex[target_lang])\n",
        "    model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True)\n",
        "\n",
        "    # Setup the tokenizer for targets\n",
        "    with tokenizer.as_target_tokenizer():\n",
        "      labels = tokenizer(targets, max_length=max_target_length, truncation=True)\n",
        "      \n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "c616b7119dbf4282a1853cd666d677dd",
            "ebfcb599607d4f038920fc0bd722d22b",
            "451e2fe9b0f84e038784d470adb45b1b",
            "c86157c035be40bb949c901774019b89",
            "cb6c731d28f940b4af0d6c6fe98bf942",
            "152674beb80b4ed5b0dd2a5bd86a3173",
            "9c6a291b8ee149f5a075f20bbaa6f2be",
            "5ec4c0bb88ff4d92a4b1157b25f1c4a3",
            "4e129cf5775d41e99e600bcca5d46b97",
            "aec559729f5546ae8352acf46a5ce423",
            "e10f90a3ab6a427c80944fca01c6bea0",
            "e789f2de644c4bd5b3fe183806b286e8",
            "79cc6152f92549a1b1c9fdfa925d2690",
            "7281f8977d3f470faf601460f4924b0d",
            "dc760cd5069b4be4b624a72d9d259adc",
            "3e0b59c236e747409ebbc2b4210ab044",
            "69d52b970faa4ed2a2db7abff0358b60",
            "06267a43550c41c0af1cb5b12de7d898",
            "b765920fd2aa46b5b7c579035b81bdb3",
            "a3efdf71ee04417b8a62cf77323c32f1",
            "000b8a59ea1942338d877c9d5c72a98b",
            "2398ef94851747a79b2637cbac3965b6"
          ]
        },
        "id": "qmaK1UX0Nwx3",
        "outputId": "df9dda3e-838c-4dd9-8ac4-2799e7fde213"
      },
      "source": [
        "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c616b7119dbf4282a1853cd666d677dd",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e789f2de644c4bd5b3fe183806b286e8",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "knRoh_8wNwx4",
        "outputId": "1fe11080-38f9-46c9-bcfd-988251daf193"
      },
      "source": [
        "tokenized_datasets"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['attention_mask', 'input_ids', 'labels', 'translation'],\n",
              "        num_rows: 408\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['attention_mask', 'input_ids', 'labels', 'translation'],\n",
              "        num_rows: 150\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eUiaZ1Y0Nwx4"
      },
      "source": [
        "# Fine-tuning the model"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HZvTYV6vaHRl"
      },
      "source": [
        "from transformers import AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq, Seq2SeqTrainingArguments, Seq2SeqTrainer\n"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qt8rECNwNwx5"
      },
      "source": [
        "batch_size = 8\n",
        "args = Seq2SeqTrainingArguments(\n",
        "    model_name,\n",
        "    evaluation_strategy = \"epoch\",\n",
        "    learning_rate=3e-4,\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    weight_decay=0.01,\n",
        "    save_total_limit=3,\n",
        "    num_train_epochs=1,\n",
        "    predict_with_generate=True,\n",
        "    fp16=fp16,\n",
        "    push_to_hub=True\n",
        ")"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wHZPXO3lNwx5"
      },
      "source": [
        "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5E_b1Wc4gbMf"
      },
      "source": [
        "import numpy as np\n",
        "def postprocess_text(preds, labels):\n",
        "    preds = [pred.replace('?',' ?').replace('.', ' .').strip() for pred in preds]\n",
        "    labels = [[label.replace('?',' ?').replace('.', ' .').strip()] for label in labels]\n",
        "    return preds, labels"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BQyXIlPMgqzO"
      },
      "source": [
        "# The last thing to define for our Seq2SeqTrainer is how to compute \n",
        "# the metrics from the predictions. We need to define \n",
        "# a function for this, which will just use the metric we loaded earlier, \n",
        "# and we have to do a bit of pre-processing to decode the predictions into texts:"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6e8FOhSBjZKM",
        "outputId": "689e60b6-4bd3-41a0-a6eb-593b2672b9d7"
      },
      "source": [
        "from bert_score import BERTScorer\n",
        "scorer = BERTScorer(lang=\"en\", rescale_with_baseline=True)\n",
        "metric = load_metric(\"sacrebleu\")"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.dense.weight', 'lm_head.decoder.weight', 'lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.layer_norm.weight', 'lm_head.bias']\n",
            "- This IS expected if you are initializing RobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CxB0Sih5fsYd"
      },
      "source": [
        "def compute_metrics(eval_preds):\n",
        "    preds, labels = eval_preds\n",
        "    if isinstance(preds, tuple):\n",
        "        preds = preds[0]\n",
        "    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
        "\n",
        "    # Replace -100 in the labels as we can't decode them.\n",
        "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
        "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
        "\n",
        "    # Some simple post-processing\n",
        "    decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)\n",
        "\n",
        "    P, R, F1 = scorer.score(decoded_preds, decoded_labels)\n",
        "    result = metric.compute(predictions=decoded_preds, references=decoded_labels)\n",
        "    \n",
        "    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]\n",
        "    gen_len = np.mean(prediction_lens)\n",
        "    \n",
        "    return {\"gen_len\":gen_len, 'P':P.mean(), 'R':R.mean(), 'F1':F1.mean(), \"bleu-score\": result[\"score\"], \"bleu-precisions\": result[\"precisions\"], \"bleu-bp\": result[\"bp\"]}"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9ZhWPx09Nwx6",
        "outputId": "91c7880b-a0e4-45a3-c800-323922e5598c"
      },
      "source": [
        "trainer = Seq2SeqTrainer(\n",
        "    model,\n",
        "    args,\n",
        "    train_dataset=tokenized_datasets[\"train\"],\n",
        "    eval_dataset=tokenized_datasets[\"test\"],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics\n",
        ")"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Cloning https://huggingface.co/yazdipour/sparql-qald9-t5-base-2021-10-19_23-02 into local empty directory.\n",
            "Using amp fp16 backend\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "qS5Q9hT2Nwx6",
        "outputId": "9e34ed1c-a09c-4653-d6e4-ba8ba2e6438e"
      },
      "source": [
        "trainer.train()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the training set  don't have a corresponding argument in `T5ForConditionalGeneration.forward` and have been ignored: translation.\n",
            "***** Running training *****\n",
            "  Num examples = 408\n",
            "  Num Epochs = 1\n",
            "  Instantaneous batch size per device = 8\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 51\n",
            "Automatic Weights & Biases logging enabled, to disable set os.environ[\"WANDB_DISABLED\"] = \"true\"\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='51' max='51' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [51/51 01:32, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Gen Len</th>\n",
              "      <th>P</th>\n",
              "      <th>R</th>\n",
              "      <th>F1</th>\n",
              "      <th>Bleu-score</th>\n",
              "      <th>Bleu-precisions</th>\n",
              "      <th>Bleu-bp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.830026</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>0.364001</td>\n",
              "      <td>0.034577</td>\n",
              "      <td>0.194338</td>\n",
              "      <td>10.035799</td>\n",
              "      <td>[72.88988261598658, 50.27455765710799, 35.93015446608462, 28.454070201643017]</td>\n",
              "      <td>0.228112</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the evaluation set  don't have a corresponding argument in `T5ForConditionalGeneration.forward` and have been ignored: translation.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 150\n",
            "  Batch size = 8\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=51, training_loss=1.2968808342428768, metrics={'train_runtime': 94.6069, 'train_samples_per_second': 4.313, 'train_steps_per_second': 0.539, 'total_flos': 52370378588160.0, 'train_loss': 1.2968808342428768, 'epoch': 1.0})"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MdHQ-R8XNwx6",
        "outputId": "94e4b250-5ea1-4c44-8a0e-35ec068cd7ce"
      },
      "source": [
        "trainer.save_model(model_path)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to ./models/sparql-qald9-t5-base-2021-10-19_23-02\n",
            "Configuration saved in ./models/sparql-qald9-t5-base-2021-10-19_23-02/config.json\n",
            "Model weights saved in ./models/sparql-qald9-t5-base-2021-10-19_23-02/pytorch_model.bin\n",
            "tokenizer config file saved in ./models/sparql-qald9-t5-base-2021-10-19_23-02/tokenizer_config.json\n",
            "Special tokens file saved in ./models/sparql-qald9-t5-base-2021-10-19_23-02/special_tokens_map.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "yteJEVG4Qyuf",
        "outputId": "71c5f434-cd95-4daa-eca7-fc3afce0d0a5"
      },
      "source": [
        "model_name"
      ],
      "execution_count": 32,
      "outputs": [
        {
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          "execution_count": 32
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          "text": [
            "Saving model checkpoint to sparql-qald9-t5-base-2021-10-19_23-02\n",
            "Configuration saved in sparql-qald9-t5-base-2021-10-19_23-02/config.json\n",
            "Model weights saved in sparql-qald9-t5-base-2021-10-19_23-02/pytorch_model.bin\n",
            "tokenizer config file saved in sparql-qald9-t5-base-2021-10-19_23-02/tokenizer_config.json\n",
            "Special tokens file saved in sparql-qald9-t5-base-2021-10-19_23-02/special_tokens_map.json\n"
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            "text/plain": [
              "Upload file pytorch_model.bin:   0%|          | 3.36k/850M [00:00<?, ?B/s]"
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          },
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        },
        {
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            "text/plain": [
              "Upload file runs/Oct19_23-08-26_4e9c45cbc667/1634684953.6451986/events.out.tfevents.1634684953.4e9c45cbc667.82…"
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              "Upload file runs/Oct19_23-08-26_4e9c45cbc667/events.out.tfevents.1634684953.4e9c45cbc667.828.0:  68%|######8  …"
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              "Upload file training_args.bin: 100%|##########| 2.92k/2.92k [00:00<?, ?B/s]"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "f_ULP1-t29Qg"
      },
      "source": [
        "raw_datasets."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VU_caa8b0Thh"
      },
      "source": [
        "translator = pipeline(\n",
        "    \"translation_xx_to_yy\",\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    device=0 #0 for cuda, -1 for cpu\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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        "translate= lambda q: (translator(prefix+q, max_length=100)[0]['translation_text'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oOWwdLUv0dNo"
      },
      "source": [
        "translate('How old is Bill Gates?')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lx4enu9908Ab"
      },
      "source": [
        "translate('Are Taiko some kind of Japanese musical instrument?  ')"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}