BERT-RACE.ipynb

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   "source": [
    "**Summary:**\n",
    "\n",
    "This notebook shows how to train a Bert model for the RACE dataset by using the Huggingface *transformers* and *datasets* libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 732,
     "status": "ok",
     "timestamp": 1604939342130,
     "user": {
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      "userId": "08728759232976284064"
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    "id": "axMB1LkSDQen",
    "outputId": "a7d7dacb-7a8b-4abe-c713-daaee44169dc"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mon Nov  9 16:28:41 2020       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 455.32.00    Driver Version: 418.67       CUDA Version: 10.1     |\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 P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
      "| N/A   37C    P0    27W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
      "|                               |                      |                 ERR! |\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"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q8KHdvHpB2Qa"
   },
   "source": [
    "**Clone and install**"
   ]
  },
  {
   "cell_type": "code",
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     "height": 1000
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     "timestamp": 1604939390021,
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      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/52/19/c2812690d8b340987eecd2cbc18549b1d130b94c5d97fcbe49f5f8710edf/ipykernel-5.3.4-py3-none-any.whl (120kB)\n",
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      "  Downloading https://files.pythonhosted.org/packages/e7/7f/470d6fcdf23f9f3518f6b0b76be9df16dcc8630ad409947f8be2eb0ed13a/pathtools-0.1.2.tar.gz\n",
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      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/69/79/e6afb3d8b0b4e96cefbdc690f741d7dd24547ff1f94240c997a26fa908d3/s3transfer-0.3.3-py2.py3-none-any.whl (69kB)\n",
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      "  Downloading https://files.pythonhosted.org/packages/b0/9a/4d409a6234eb940e6a78dfdfc66156e7522262f5f2fecca07dc55915952d/smmap-3.0.4-py2.py3-none-any.whl\n",
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      "Building wheels for collected packages: seqeval, fire, PyYAML, blinker, watchdog, pathtools\n",
      "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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      "  Stored in directory: /root/.cache/pip/wheels/a7/c1/ea/cf5bd31012e735dc1dfea3131a2d5eae7978b251083d6247bd\n",
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      "  Stored in directory: /root/.cache/pip/wheels/0b/04/79/c3b0c3a0266a3cb4376da31e5bfe8bba0c489246968a68e843\n",
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      "\u001b[31mERROR: google-colab 1.0.0 has requirement ipykernel~=4.10, but you'll have ipykernel 5.3.4 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: pytorch-lightning 1.0.4 has requirement future>=0.17.1, but you'll have future 0.16.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: botocore 1.19.13 has requirement urllib3<1.26,>=1.25.4; python_version != \"3.4\", but you'll have urllib3 1.24.3 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: datasets 1.1.2 has requirement pyarrow>=0.17.1, but you'll have pyarrow 0.14.1 which is incompatible.\u001b[0m\n",
      "Installing collected packages: seqeval, portalocker, sacrebleu, rouge-score, PyYAML, fsspec, pytorch-lightning, smmap, gitdb, gitpython, git-python, faiss-cpu, jmespath, botocore, base58, blinker, ipykernel, pydeck, pathtools, watchdog, enum-compat, s3transfer, boto3, validators, streamlit, elasticsearch, xxhash, datasets, fire, conllu\n",
      "  Found existing installation: PyYAML 3.13\n",
      "    Uninstalling PyYAML-3.13:\n",
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      "  Found existing installation: ipykernel 4.10.1\n",
      "    Uninstalling ipykernel-4.10.1:\n",
      "      Successfully uninstalled ipykernel-4.10.1\n",
      "Successfully installed PyYAML-5.3.1 base58-2.0.1 blinker-1.4 boto3-1.16.13 botocore-1.19.13 conllu-4.2.1 datasets-1.1.2 elasticsearch-7.9.1 enum-compat-0.0.3 faiss-cpu-1.6.4.post2 fire-0.3.1 fsspec-0.8.4 git-python-1.0.3 gitdb-4.0.5 gitpython-3.1.11 ipykernel-5.3.4 jmespath-0.10.0 pathtools-0.1.2 portalocker-2.0.0 pydeck-0.5.0 pytorch-lightning-1.0.4 rouge-score-0.0.4 s3transfer-0.3.3 sacrebleu-1.4.14 seqeval-1.2.2 smmap-3.0.4 streamlit-0.70.0 validators-0.18.1 watchdog-0.10.3 xxhash-2.0.0\n"
     ]
    },
    {
     "data": {
      "application/vnd.colab-display-data+json": {
       "pip_warning": {
        "packages": [
         "ipykernel"
        ]
       }
      }
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/huggingface/transformers\n",
    "%cd transformers\n",
    "!pip install .\n",
    "!pip install -r ./examples/requirements.txt\n",
    "%cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 59527,
     "status": "ok",
     "timestamp": 1604939400958,
     "user": {
      "displayName": "Marcos Martinez",
      "photoUrl": "",
      "userId": "08728759232976284064"
     },
     "user_tz": -60
    },
    "id": "iXFLOjg8pz-H",
    "outputId": "d9b20b80-3fbd-4e9b-b2e1-7fc4a2181d44"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: datasets in /usr/local/lib/python3.6/dist-packages (1.1.2)\n",
      "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from datasets) (1.1.4)\n",
      "Requirement already satisfied: multiprocess in /usr/local/lib/python3.6/dist-packages (from datasets) (0.70.10)\n",
      "Requirement already satisfied: filelock in /usr/local/lib/python3.6/dist-packages (from datasets) (3.0.12)\n",
      "Requirement already satisfied: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from datasets) (0.7)\n",
      "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.6/dist-packages (from datasets) (1.18.5)\n",
      "Requirement already satisfied: xxhash in /usr/local/lib/python3.6/dist-packages (from datasets) (2.0.0)\n",
      "Collecting pyarrow>=0.17.1\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d7/e1/27958a70848f8f7089bff8d6ebe42519daf01f976d28b481e1bfd52c8097/pyarrow-2.0.0-cp36-cp36m-manylinux2014_x86_64.whl (17.7MB)\n",
      "\u001b[K     |████████████████████████████████| 17.7MB 212kB/s \n",
      "\u001b[?25hRequirement already satisfied: dill in /usr/local/lib/python3.6/dist-packages (from datasets) (0.3.3)\n",
      "Requirement already satisfied: tqdm<4.50.0,>=4.27 in /usr/local/lib/python3.6/dist-packages (from datasets) (4.41.1)\n",
      "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.6/dist-packages (from datasets) (2.23.0)\n",
      "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->datasets) (2018.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas->datasets) (2.8.1)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->datasets) (1.24.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->datasets) (2020.6.20)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->datasets) (3.0.4)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->datasets) (2.10)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n",
      "Installing collected packages: pyarrow\n",
      "  Found existing installation: pyarrow 0.14.1\n",
      "    Uninstalling pyarrow-0.14.1:\n",
      "      Successfully uninstalled pyarrow-0.14.1\n",
      "Successfully installed pyarrow-2.0.0\n"
     ]
    }
   ],
   "source": [
    "!pip install datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 59525,
     "status": "ok",
     "timestamp": 1604939400970,
     "user": {
      "displayName": "Marcos Martinez",
      "photoUrl": "",
      "userId": "08728759232976284064"
     },
     "user_tz": -60
    },
    "id": "UhwiUjAMqzJr",
    "outputId": "7d758ec4-f48b-4c6d-8257-9f7eec6d9c01"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content/transformers/examples/multiple-choice\n"
     ]
    }
   ],
   "source": [
    "cd transformers/examples/multiple-choice/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8oCh2msxA8Gz"
   },
   "source": [
    "**Generic imports**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Acq3GXvDqW7i"
   },
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-EbnJKS7A_Th"
   },
   "source": [
    "**Huggingface imports**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "GQphBbtSqk6n"
   },
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "from transformers import (\n",
    "    AutoConfig,\n",
    "    AutoModelForMultipleChoice,\n",
    "    AutoTokenizer,\n",
    "    EvalPrediction,\n",
    "    HfArgumentParser,\n",
    "    Trainer,\n",
    "    TrainingArguments,\n",
    "    set_seed,\n",
    ")\n",
    "\n",
    "from utils_multiple_choice import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 65458,
     "status": "ok",
     "timestamp": 1604939406929,
     "user": {
      "displayName": "Marcos Martinez",
      "photoUrl": "",
      "userId": "08728759232976284064"
     },
     "user_tz": -60
    },
    "id": "EudACmOEOu0Y",
    "outputId": "4f0d96ac-2636-4e02-c019-9dbe8ea85b34"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content\n"
     ]
    }
   ],
   "source": [
    "cd ../../.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 92645,
     "status": "ok",
     "timestamp": 1604939434132,
     "user": {
      "displayName": "Marcos Martinez",
      "photoUrl": "",
      "userId": "08728759232976284064"
     },
     "user_tz": -60
    },
    "id": "a2W0LJiKc2uR",
    "outputId": "c5e7a3d8-602d-40b6-b225-ad99c252f9de"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V2ClzgocBG6s"
   },
   "source": [
    "**Load dataset and model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "executionInfo": {
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     "status": "ok",
     "timestamp": 1604939477070,
     "user": {
      "displayName": "Marcos Martinez",
      "photoUrl": "",
      "userId": "08728759232976284064"
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     "user_tz": -60
    },
    "id": "o3iY8-AyqqP8",
    "outputId": "43196465-19ce-462c-be8a-3127f36aef16"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0c919f089b9d494a9abbee9a6f15d203",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1605.0, style=ProgressStyle(description…"
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     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Downloading and preparing dataset race/middle (download: 24.26 MiB, generated: 33.01 MiB, post-processed: Unknown size, total: 57.27 MiB) to /root/.cache/huggingface/datasets/race/middle/0.1.0/a7d1fac780e70c0e75bca35e9f2f8cfc1411edd18ffd6858ddce56f70dfb1e7c...\n"
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    {
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     },
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     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ca169123f6684d2fa04d6b47c01d3d37",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
    {
     "data": {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Dataset race downloaded and prepared to /root/.cache/huggingface/datasets/race/middle/0.1.0/a7d1fac780e70c0e75bca35e9f2f8cfc1411edd18ffd6858ddce56f70dfb1e7c. Subsequent calls will reuse this data.\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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       "model_id": "de784552779d4dd6a0bb3b1ba4aae9d4",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMultipleChoice: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
      "- This IS expected if you are initializing BertForMultipleChoice 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 BertForMultipleChoice from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of BertForMultipleChoice were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0f068d17266c405babd34bdf0c3a9a8f",
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       "version_minor": 0
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "dataset = load_dataset(\"race\", \"middle\")\n",
    "model = AutoModelForMultipleChoice.from_pretrained('bert-base-uncased')\n",
    "tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9y31ANZuBOqZ"
   },
   "source": [
    "**Training args**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZPiQkKr_sQKv"
   },
   "outputs": [],
   "source": [
    "label_list = [\"0\", \"1\", \"2\", \"3\"]\n",
    "max_seq_length = 468\n",
    "\n",
    "batch_size = 4\n",
    "training_args = TrainingArguments(\n",
    "  \"test-multichoice\",\n",
    "  evaluation_strategy = \"epoch\",\n",
    "  learning_rate=2e-5,\n",
    "  per_device_train_batch_size=batch_size,\n",
    "  per_device_eval_batch_size=batch_size,\n",
    "  num_train_epochs=3,\n",
    "  weight_decay=0.01,\n",
    "  load_best_model_at_end=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vfJK4AiXBTDI"
   },
   "source": [
    "**Create examples from HuggingFace Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "m5ymuY-tsbR1"
   },
   "outputs": [],
   "source": [
    "train_examples = []\n",
    "data_train = dataset['train']\n",
    "for ex in data_train:\n",
    "    race_id = ex[\"example_id\"]\n",
    "    article = ex[\"article\"]\n",
    "\n",
    "    truth = str(ord(ex[\"answer\"]) - ord(\"A\"))\n",
    "    question = ex[\"question\"]\n",
    "    options = ex[\"options\"]\n",
    "\n",
    "    train_examples.append(\n",
    "      InputExample(\n",
    "          example_id=race_id,\n",
    "          question=question,\n",
    "          contexts=[article, article, article, article],  # this is not efficient but convenient\n",
    "          endings=[options[0], options[1], options[2], options[3]],\n",
    "          label=truth,\n",
    "      )\n",
    "    )\n",
    "\n",
    "val_examples = []\n",
    "data_val = dataset['validation']\n",
    "for ex in data_val:\n",
    "    race_id = ex[\"example_id\"]\n",
    "    article = ex[\"article\"]\n",
    "\n",
    "    truth = str(ord(ex[\"answer\"]) - ord(\"A\"))\n",
    "    question = ex[\"question\"]\n",
    "    options = ex[\"options\"]\n",
    "\n",
    "    val_examples.append(\n",
    "      InputExample(\n",
    "          example_id=race_id,\n",
    "          question=question,\n",
    "          contexts=[article, article, article, article],  # this is not efficient but convenient\n",
    "          endings=[options[0], options[1], options[2], options[3]],\n",
    "          label=truth,\n",
    "      )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ttsn5nzzBZoZ"
   },
   "source": [
    "**Convert examples to features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 570982,
     "status": "ok",
     "timestamp": 1604939916071,
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    "id": "xEk7ODK0sNCK",
    "outputId": "ac53b83a-4112-4530-b65a-327e166189df"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "convert examples to features: 25421it [06:54, 61.37it/s]\n",
      "convert examples to features: 1436it [00:23, 61.29it/s]\n"
     ]
    }
   ],
   "source": [
    "train_features = convert_examples_to_features(\n",
    "    train_examples,\n",
    "    label_list,\n",
    "    max_seq_length,\n",
    "    tokenizer,\n",
    ")\n",
    "\n",
    "val_features = convert_examples_to_features(\n",
    "    val_examples,\n",
    "    label_list,\n",
    "    max_seq_length,\n",
    "    tokenizer,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vQls9VTGBc8n"
   },
   "source": [
    "**Create trainer**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5rnvZjuKr7ME"
   },
   "outputs": [],
   "source": [
    "def simple_accuracy(preds, labels):\n",
    "    return (preds == labels).mean()\n",
    "    \n",
    "def compute_metrics(p):\n",
    "    preds = np.argmax(p.predictions, axis=1)\n",
    "    return {\"acc\": simple_accuracy(preds, p.label_ids)}\n",
    "\n",
    "# Initialize our Trainerlo\n",
    "trainer = Trainer(\n",
    "  model=model,\n",
    "  args=training_args,\n",
    "  train_dataset=train_features,\n",
    "  eval_dataset=val_features,\n",
    "  compute_metrics=compute_metrics,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Znviko-tBe2B"
   },
   "source": [
    "**Train and eval**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "executionInfo": {
     "elapsed": 16151057,
     "status": "ok",
     "timestamp": 1604955497838,
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    },
    "id": "qPzivHt6xvep",
    "outputId": "7f495d43-3637-4c8e-c937-79a3c292ea5f"
   },
   "outputs": [
    {
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       "        </style>\n",
       "      \n",
       "      <progress value='19068' max='19068' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [19068/19068 4:19:26, Epoch 3/3]\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>Acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
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       "      [359/359 01:34]\n",
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   "cell_type": "markdown",
   "metadata": {
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   "source": [
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "woNeNQ1TLtG-"
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    "try:\n",
    "    trainer.save_model(\"drive/My Drive/bbu_race_m/\")\n",
    "    tokenizer.save_pretrained(\"drive/My Drive/bbu_race_m/\")\n",
    "except:\n",
    "    trainer.save_model(\"bbu_race_m/\")\n",
    "    tokenizer.save_pretrained(\"bbu_race_m/\")"
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