text_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X4cRE8IbIrIV"
   },
   "source": [
    "If you're opening this Notebook on colab, you will probably need to install 🤗 Transformers and 🤗 Datasets. Uncomment the following cell and run it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "MOsHUjgdIrIW",
    "outputId": "f84a093e-147f-470e-aad9-80fb51193c8e"
   },
   "outputs": [],
   "source": [
    "#! pip install datasets transformers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HFASsisvIrIb"
   },
   "source": [
    "If you're opening this notebook locally, make sure your environment has the last version of those libraries installed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rEJBSTyZIrIb"
   },
   "source": [
    "# Fine-tuning a model on a text classification task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kTCFado4IrIc"
   },
   "source": [
    "In this notebook, we will see how to fine-tune one of the [🤗 Transformers](https://github.com/huggingface/transformers) model to a text classification task of the [GLUE Benchmark](https://gluebenchmark.com/), [MRPC](https://www.microsoft.com/en-us/download/details.aspx?id=52398).\n",
    "\n",
    "MRPC stands for Microsoft Research Paraphrase Corpus, and is a dataset containing pair of sentences where the task is to determine if the two sentences in the pair are paraphrases of one another or not.\n",
    "\n",
    "We will see how to easily load the dataset for this task using 🤗 Datasets and how to fine-tune a model on it using the `Trainer` API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_checkpoint = \"distilbert-base-uncased\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4RRkXuteIrIh"
   },
   "source": [
    "This notebook is built to run  with any model checkpoint from the [Model Hub](https://huggingface.co/models) as long as that model has a version with a classification head in the Transformers library. Here we picked the [`distilbert-base-uncased`](https://huggingface.co/distilbert-base-uncased) checkpoint. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "whPRbBNbIrIl"
   },
   "source": [
    "## Loading the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W7QYTpxXIrIl"
   },
   "source": [
    "We will use the [🤗 Datasets](https://github.com/huggingface/datasets) library to download the data and get the metric we need to use for evaluation (to compare our model to the benchmark). This can be easily done with the functions `load_dataset` and `load_metric`.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "IreSlFmlIrIm"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Reusing dataset glue (/home/sgugger/.cache/huggingface/datasets/glue/mrpc/1.0.0/7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4)\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset, load_metric\n",
    "\n",
    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
    "metric = load_metric(\"glue\", \"mrpc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RzfPtOMoIrIu"
   },
   "source": [
    "The `dataset` object itself is [`DatasetDict`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasetdict), which contains one key for the training, validation and test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "GWiVUF0jIrIv",
    "outputId": "35e3ea43-f397-4a54-c90c-f2cf8d36873e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
       "        num_rows: 3668\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
       "        num_rows: 408\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
       "        num_rows: 1725\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u3EtYfeHIrIz"
   },
   "source": [
    "To access an actual element, you need to select a split first, then give an index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "X6HrpprwIrIz",
    "outputId": "d7670bc0-42e4-4c09-8a6a-5c018ded7d95"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'idx': 0,\n",
       " 'label': 1,\n",
       " 'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
       " 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_datasets[\"train\"][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WHUmphG3IrI3"
   },
   "source": [
    "To get a sense of what the data looks like, the following function will show some examples picked randomly in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "i3j8APAoIrI3"
   },
   "outputs": [],
   "source": [
    "import datasets\n",
    "import random\n",
    "import pandas as pd\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "def show_random_elements(dataset, num_examples=10):\n",
    "    assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\n",
    "    picks = []\n",
    "    for _ in range(num_examples):\n",
    "        pick = random.randint(0, len(dataset)-1)\n",
    "        while pick in picks:\n",
    "            pick = random.randint(0, len(dataset)-1)\n",
    "        picks.append(pick)\n",
    "    \n",
    "    df = pd.DataFrame(dataset[picks])\n",
    "    for column, typ in dataset.features.items():\n",
    "        if isinstance(typ, datasets.ClassLabel):\n",
    "            df[column] = df[column].transform(lambda i: typ.names[i])\n",
    "    display(HTML(df.to_html()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "SZy5tRB_IrI7",
    "outputId": "ba8f2124-e485-488f-8c0c-254f34f24f13"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idx</th>\n",
       "      <th>label</th>\n",
       "      <th>sentence1</th>\n",
       "      <th>sentence2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1065</td>\n",
       "      <td>equivalent</td>\n",
       "      <td>SSE shares were little changed , up 0.3 percent at 654 pence by 1018 GMT , but analysts welcomed the move .</td>\n",
       "      <td>SSE shares were unchanged at 652 pence by 0835 GMT but analysts welcomed the deal .</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4059</td>\n",
       "      <td>not_equivalent</td>\n",
       "      <td>In 2002 , Linksys overtook Cisco Systems as the leading wireless equipment vendor , accounting for 14.1 percent of revenue .</td>\n",
       "      <td>Rolfe said Linksys overtook Cisco Systems last year as the leading supplier of WLAN equipment .</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2148</td>\n",
       "      <td>equivalent</td>\n",
       "      <td>Yesterday , two Japanese diplomats were killed in an apparent ambush near Tikrit , 175 kilometres north of the capital .</td>\n",
       "      <td>Two Japanese diplomats were also killed in an ambush near Tikrit , according to the Japanese Foreign Ministry .</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>667</td>\n",
       "      <td>not_equivalent</td>\n",
       "      <td>Under the settlement , Solutia will pay $ 50 million in equal installments over a period of 10 years .</td>\n",
       "      <td>Under the agreement , Solutia will pay $ 50 million and Monsanto will pay $ 390 million .</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3073</td>\n",
       "      <td>equivalent</td>\n",
       "      <td>Southwest , the largest U.S. discount airline , said Friday that inspections of its fleet of 385 planes had found no additional items .</td>\n",
       "      <td>Southwest said it completed inspections of its entire fleet of 385 aircraft and found no additional items .</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3737</td>\n",
       "      <td>equivalent</td>\n",
       "      <td>It is publishing the studies in the July issue of its Journal of Allergy and Clinical Immunology .</td>\n",
       "      <td>The reports are among several published this week in the Journal of Allergy and Clinical Immunology .</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>763</td>\n",
       "      <td>equivalent</td>\n",
       "      <td>President Bush and top officials in his administration cited the threat from Iraq 's alleged chemical and biological weapons and nuclear weapons program as the main justification for going to war .</td>\n",
       "      <td>President George Bush and top administration officials cited the threat from Iraq 's banned weapons programs as the main justification for war .</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3094</td>\n",
       "      <td>equivalent</td>\n",
       "      <td>Under state law , DeVries must be released to the jurisdiction in which he was convicted .</td>\n",
       "      <td>Under state policy , DeVries was to be returned to San Jose , where he was last convicted .</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1864</td>\n",
       "      <td>equivalent</td>\n",
       "      <td>They made a similar discovery in Houston on another aircraft , Dallas-based Southwest said in a statement .</td>\n",
       "      <td>The airline said a similar discovery was made on the plane in Houston .</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2549</td>\n",
       "      <td>equivalent</td>\n",
       "      <td>Prosecutors contended that Mr. Durst had plotted the murder to assume Mr. Black 's identity .</td>\n",
       "      <td>Prosecutors maintained that Durst murdered Black to try to assume Black 's identity .</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_random_elements(raw_datasets[\"train\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lnjDIuQ3IrI-"
   },
   "source": [
    "The metric is an instance of [`datasets.Metric`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Metric):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "5o4rUteaIrI_",
    "outputId": "18038ef5-554c-45c5-e00a-133b02ec10f1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Metric(name: \"glue\", features: {'predictions': Value(dtype='int64', id=None), 'references': Value(dtype='int64', id=None)}, usage: \"\"\"\n",
       "Compute GLUE evaluation metric associated to each GLUE dataset.\n",
       "Args:\n",
       "    predictions: list of predictions to score.\n",
       "        Each translation should be tokenized into a list of tokens.\n",
       "    references: list of lists of references for each translation.\n",
       "        Each reference should be tokenized into a list of tokens.\n",
       "Returns: depending on the GLUE subset, one or several of:\n",
       "    \"accuracy\": Accuracy\n",
       "    \"f1\": F1 score\n",
       "    \"pearson\": Pearson Correlation\n",
       "    \"spearmanr\": Spearman Correlation\n",
       "    \"matthews_correlation\": Matthew Correlation\n",
       "Examples:\n",
       "\n",
       "    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n",
       "    >>> references = [0, 1]\n",
       "    >>> predictions = [0, 1]\n",
       "    >>> results = glue_metric.compute(predictions=predictions, references=references)\n",
       "    >>> print(results)\n",
       "    {'accuracy': 1.0}\n",
       "\n",
       "    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'\n",
       "    >>> references = [0, 1]\n",
       "    >>> predictions = [0, 1]\n",
       "    >>> results = glue_metric.compute(predictions=predictions, references=references)\n",
       "    >>> print(results)\n",
       "    {'accuracy': 1.0, 'f1': 1.0}\n",
       "\n",
       "    >>> glue_metric = datasets.load_metric('glue', 'stsb')\n",
       "    >>> references = [0., 1., 2., 3., 4., 5.]\n",
       "    >>> predictions = [0., 1., 2., 3., 4., 5.]\n",
       "    >>> results = glue_metric.compute(predictions=predictions, references=references)\n",
       "    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n",
       "    {'pearson': 1.0, 'spearmanr': 1.0}\n",
       "\n",
       "    >>> glue_metric = datasets.load_metric('glue', 'cola')\n",
       "    >>> references = [0, 1]\n",
       "    >>> predictions = [0, 1]\n",
       "    >>> results = glue_metric.compute(predictions=predictions, references=references)\n",
       "    >>> print(results)\n",
       "    {'matthews_correlation': 1.0}\n",
       "\"\"\", stored examples: 0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jAWdqcUBIrJC"
   },
   "source": [
    "You can call its `compute` method with your predictions and labels directly and it will return a dictionary with the metric(s) value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "6XN1Rq0aIrJC",
    "outputId": "a4405435-a8a9-41ff-9f79-a13077b587c7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.390625, 'f1': 0.380952380952381}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "fake_preds = np.random.randint(0, 2, size=(64,))\n",
    "fake_labels = np.random.randint(0, 2, size=(64,))\n",
    "metric.compute(predictions=fake_preds, references=fake_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YOCrQwPoIrJG"
   },
   "source": [
    "Note that `load_metric` has loaded the proper metric associated to our specific task in the GLUE benchmark, here accuracy and [F1 score](https://en.wikipedia.org/wiki/F1_score)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n9qywopnIrJH"
   },
   "source": [
    "## Preprocessing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YVx71GdAIrJH"
   },
   "source": [
    "Before we can feed those texts to our model, we need to preprocess them. This is done by a 🤗 Transformers `Tokenizer` which will (as the name indicates) tokenize the inputs (including converting the tokens to their corresponding IDs in the pretrained vocabulary) and put it in a format the model expects, as well as generate the other inputs that model requires.\n",
    "\n",
    "To do all of this, we instantiate our tokenizer with the `AutoTokenizer.from_pretrained` method, which will ensure:\n",
    "\n",
    "- we get a tokenizer that corresponds to the model architecture we want to use,\n",
    "- we download the vocabulary used when pretraining this specific checkpoint.\n",
    "\n",
    "That vocabulary will be cached, so it's not downloaded again the next time we run the cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "eXNLu_-nIrJI"
   },
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer\n",
    "    \n",
    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Vl6IidfdIrJK"
   },
   "source": [
    "Bu default, the call above will use one of the fast tokenizers (backed by Rust) from the 🤗 Tokenizers library."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rowT4iCLIrJK"
   },
   "source": [
    "You can directly call this tokenizer on one sentence or a pair of sentences:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "a5hBlsrHIrJL",
    "outputId": "acdaa98a-a8cd-4a20-89b8-cc26437bbe90"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': [101, 7592, 1010, 2023, 2028, 6251, 999, 102, 1998, 2023, 6251, 3632, 2007, 2009, 1012, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer(\"Hello, this one sentence!\", \"And this sentence goes with it.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qo_0B1M2IrJM"
   },
   "source": [
    "Depending on the model you selected, you will see different keys in the dictionary returned by the cell above. They don't matter much for what we're doing here (just know they are required by the model we will instantiate later), you can learn more about them in [this tutorial](https://huggingface.co/transformers/preprocessing.html) if you're interested.\n",
    "\n",
    "Instead of one pair of sentences, we can pass along a list of first sentences and list of second sentences:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': [[101, 7592, 1010, 2023, 2028, 6251, 999, 102, 2023, 6251, 3632, 2007, 2034, 1012, 102], [101, 2023, 6251, 2003, 2117, 1012, 102, 2023, 2028, 3632, 2007, 2117, 1012, 102]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer([\"Hello, this one sentence!\", \"This sentence is second.\"], \n",
    "          [\"This sentence goes with first.\", \"This one goes with second.\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2C0hcmp9IrJQ"
   },
   "source": [
    "We can them write the function that will preprocess our samples. We just feed them to the `tokenizer` with the argument `truncation=True`. This will ensure that an input longer that what the model selected can handle will be truncated to the maximum length accepted by the model. The padding will be dealt with later on (in a data collator) so we pad examples to the longest length in the batch and not the whole dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "vc0BSBLIIrJQ"
   },
   "outputs": [],
   "source": [
    "def preprocess_function(examples):\n",
    "    return tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0lm8ozrJIrJR"
   },
   "source": [
    "This function works with one or several examples. In the case of several examples, the tokenizer will return a list of lists for each key:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "-b70jh26IrJS",
    "outputId": "acd3a42d-985b-44ee-9daa-af5d944ce1d9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': [[101, 2572, 3217, 5831, 5496, 2010, 2567, 1010, 3183, 2002, 2170, 1000, 1996, 7409, 1000, 1010, 1997, 9969, 4487, 23809, 3436, 2010, 3350, 1012, 102, 7727, 2000, 2032, 2004, 2069, 1000, 1996, 7409, 1000, 1010, 2572, 3217, 5831, 5496, 2010, 2567, 1997, 9969, 4487, 23809, 3436, 2010, 3350, 1012, 102], [101, 9805, 3540, 11514, 2050, 3079, 11282, 2243, 1005, 1055, 2077, 4855, 1996, 4677, 2000, 3647, 4576, 1999, 2687, 2005, 1002, 1016, 1012, 1019, 4551, 1012, 102, 9805, 3540, 11514, 2050, 4149, 11282, 2243, 1005, 1055, 1999, 2786, 2005, 1002, 6353, 2509, 2454, 1998, 2853, 2009, 2000, 3647, 4576, 2005, 1002, 1015, 1012, 1022, 4551, 1999, 2687, 1012, 102], [101, 2027, 2018, 2405, 2019, 15147, 2006, 1996, 4274, 2006, 2238, 2184, 1010, 5378, 1996, 6636, 2005, 5096, 1010, 2002, 2794, 1012, 102, 2006, 2238, 2184, 1010, 1996, 2911, 1005, 1055, 5608, 2018, 2405, 2019, 15147, 2006, 1996, 4274, 1010, 5378, 1996, 14792, 2005, 5096, 1012, 102], [101, 2105, 6021, 19481, 13938, 2102, 1010, 21628, 6661, 2020, 2039, 2539, 16653, 1010, 2030, 1018, 1012, 1018, 1003, 1010, 2012, 1037, 1002, 1018, 1012, 5179, 1010, 2383, 3041, 2275, 1037, 2501, 2152, 1997, 1037, 1002, 1018, 1012, 5401, 1012, 102, 21628, 6661, 5598, 2322, 16653, 1010, 2030, 1018, 1012, 1020, 1003, 1010, 2000, 2275, 1037, 2501, 5494, 2152, 2012, 1037, 1002, 1018, 1012, 5401, 1012, 102], [101, 1996, 4518, 3123, 1002, 1016, 1012, 2340, 1010, 2030, 2055, 2340, 3867, 1010, 2000, 2485, 5958, 2012, 1002, 2538, 1012, 4868, 2006, 1996, 2047, 2259, 4518, 3863, 1012, 102, 18720, 1004, 1041, 13058, 1012, 6661, 5598, 1002, 1015, 1012, 6191, 2030, 1022, 3867, 2000, 1002, 2538, 1012, 6021, 2006, 1996, 2047, 2259, 4518, 3863, 2006, 5958, 1012, 102]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocess_function(raw_datasets['train'][:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zS-6iXTkIrJT"
   },
   "source": [
    "To apply this function on all the pairs of sentences in our dataset, we just use the `map` method of our `dataset` object we created earlier. This will apply the function on all the elements of all the splits in `dataset`, so our training, validation and testing data will be preprocessed in one single command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "DDtsaJeVIrJT",
    "outputId": "aa4734bf-4ef5-4437-9948-2c16363da719"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /home/sgugger/.cache/huggingface/datasets/glue/mrpc/1.0.0/7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4/cache-d5034a2a4242a6a2.arrow\n",
      "Loading cached processed dataset at /home/sgugger/.cache/huggingface/datasets/glue/mrpc/1.0.0/7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4/cache-9502ead6378970b1.arrow\n",
      "Loading cached processed dataset at /home/sgugger/.cache/huggingface/datasets/glue/mrpc/1.0.0/7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4/cache-a9c5a6d873d537a2.arrow\n"
     ]
    }
   ],
   "source": [
    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "voWiw8C7IrJV"
   },
   "source": [
    "Even better, the results are automatically cached by the 🤗 Datasets library to avoid spending time on this step the next time you run your notebook. The 🤗 Datasets library is normally smart enough to detect when the function you pass to map has changed (and thus requires to not use the cache data). For instance, it will properly detect if you change the task in the first cell and rerun the notebook. 🤗 Datasets warns you when it uses cached files, you can pass `load_from_cache_file=False` in the call to `map` to not use the cached files and force the preprocessing to be applied again.\n",
    "\n",
    "Note that we passed `batched=True` to encode the texts by batches together. This is to leverage the full benefit of the fast tokenizer we loaded earlier, which will use multi-threading to treat the texts in a batch concurrently."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "545PP3o8IrJV"
   },
   "source": [
    "## Fine-tuning the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FBiW8UpKIrJW"
   },
   "source": [
    "Now that our data is ready, we can download the pretrained model and fine-tune it. Since all our task is about sentence classification, we use the `AutoModelForSequenceClassification` class. Like with the tokenizer, the `from_pretrained` method will download and cache the model for us. The only thing we have to specify is the number of labels for our problem (which is 2 here):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "TlqNaB8jIrJW",
    "outputId": "84916cf3-6e6c-47f3-d081-032ec30a4132"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_transform.bias', 'vocab_layer_norm.weight', 'vocab_layer_norm.bias', 'vocab_projector.weight', 'vocab_projector.bias']\n",
      "- This IS expected if you are initializing DistilBertForSequenceClassification 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 DistilBertForSequenceClassification 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 DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'pre_classifier.bias', '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"
     ]
    }
   ],
   "source": [
    "from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer\n",
    "\n",
    "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CczA5lJlIrJX"
   },
   "source": [
    "The warning is telling us we are throwing away some weights (the `vocab_transform` and `vocab_layer_norm` layers) and randomly initializing some other (the `pre_classifier` and `classifier` layers). This is absolutely normal in this case, because we are removing the head used to pretrain the model on a masked language modeling objective and replacing it with a new head for which we don't have pretrained weights, so the library warns us we should fine-tune this model before using it for inference, which is exactly what we are going to do."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_N8urzhyIrJY"
   },
   "source": [
    "To instantiate a `Trainer`, we will need to define two more things. The most important is the [`TrainingArguments`](https://huggingface.co/transformers/main_classes/trainer.html#transformers.TrainingArguments), which is a class that contains all the attributes to customize the training. It requires one folder name, which will be used to save the checkpoints of the model, and all other arguments are optional:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "Bliy8zgjIrJY"
   },
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "args = TrainingArguments(\n",
    "    \"test-glue\",\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=5,\n",
    "    weight_decay=0.01,\n",
    "    load_best_model_at_end=True,\n",
    "    metric_for_best_model=\"accuracy\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "km3pGVdTIrJc"
   },
   "source": [
    "Here we set the evaluation to be done at the end of each epoch, tweak the learning rate, use the `batch_size` defined at the top of the cell and customize the number of epochs for training, as well as the weight decay. Since the best model might not be the one at the end of training, we ask the `Trainer` to load the best model it saved (according to `metric_name`) at the end of training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7sZOdRlRIrJd"
   },
   "source": [
    "The last thing to define for our `Trainer` is how to compute the metrics from the predictions. We need to define a function for this, which will just use the `metric` we loaded earlier, the only preprocessing we have to do is to take the argmax of our predicted logits:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "UmvbnJ9JIrJd"
   },
   "outputs": [],
   "source": [
    "def compute_metrics(eval_pred):\n",
    "    predictions, labels = eval_pred\n",
    "    predictions = np.argmax(predictions, axis=1)\n",
    "    return metric.compute(predictions=predictions, references=labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rXuFTAzDIrJe"
   },
   "source": [
    "Then we just need to pass all of this along with our datasets to the `Trainer`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "imY1oC3SIrJf"
   },
   "outputs": [],
   "source": [
    "trainer = Trainer(\n",
    "    model,\n",
    "    args,\n",
    "    train_dataset=tokenized_datasets[\"train\"],\n",
    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
    "    tokenizer=tokenizer,\n",
    "    compute_metrics=compute_metrics\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ibWGmvxbIrJg"
   },
   "source": [
    "You might wonder why we pass along the `tokenizer` when we already preprocessed our data. This is because we will use it once last time to make all the samples we gather the same length by applying padding, which requires knowing the model's preferences regarding padding (to the left or right? with which token?). The `tokenizer` has a pad method that will do all of this right for us, and the `Trainer` will use it. You can customize this part by defining and passing your own `data_collator` which will receive the samples like the dictionaries seen above and will need to return a dictionary of tensors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CdzABDVcIrJg"
   },
   "source": [
    "We can now finetune our model by just calling the `train` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "uNx5pyRlIrJh",
    "outputId": "077e661e-d36c-469b-89b8-7ff7f73541ec"
   },
   "outputs": [
    {
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       "      [575/575 01:01, Epoch 5/5]\n",
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       "    <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>Accuracy</th>\n",
       "      <th>F1</th>\n",
       "      <th>Runtime</th>\n",
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       "      <td>No log</td>\n",
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       "      <td>0.825980</td>\n",
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       "      <td>No log</td>\n",
       "      <td>0.369089</td>\n",
       "      <td>0.835784</td>\n",
       "      <td>0.882250</td>\n",
       "      <td>0.309400</td>\n",
       "      <td>1318.554000</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.432965</td>\n",
       "      <td>0.840686</td>\n",
       "      <td>0.889643</td>\n",
       "      <td>0.310100</td>\n",
       "      <td>1315.566000</td>\n",
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       "      <td>5</td>\n",
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       "      <td>0.443647</td>\n",
       "      <td>0.840686</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.310500</td>\n",
       "      <td>1314.205000</td>\n",
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      "text/plain": [
       "TrainOutput(global_step=575, training_loss=0.32818156698475714, metrics={'train_runtime': 61.9265, 'train_samples_per_second': 9.285, 'total_flos': 589824894852720.0, 'epoch': 5.0, 'init_mem_cpu_alloc_delta': 317394, 'init_mem_gpu_alloc_delta': 268953088, 'init_mem_cpu_peaked_delta': 18306, 'init_mem_gpu_peaked_delta': 0, 'train_mem_cpu_alloc_delta': 789408, 'train_mem_gpu_alloc_delta': 1083066880, 'train_mem_cpu_peaked_delta': 187765810, 'train_mem_gpu_peaked_delta': 1118876672})"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CKASz-2vIrJi"
   },
   "source": [
    "We can check with the `evaluate` method that our `Trainer` did reload the best model properly (if it was not the last one):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "UOUcBkX8IrJi",
    "outputId": "de5b9dd6-9dc0-4702-cb43-55e9829fde25"
   },
   "outputs": [
    {
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       " 'eval_accuracy': 0.8406862745098039,\n",
       " 'eval_f1': 0.8896434634974534,\n",
       " 'eval_runtime': 0.3074,\n",
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   ],
   "source": [
    "trainer.evaluate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check all our Hugging Face [notebook examples](https://huggingface.co/transformers/notebooks.html) and the regular [script examples](https://github.com/huggingface/transformers/tree/master/examples)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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