{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sKB26q-4-X8r"
      },
      "source": [
        "# CS224N: Hugging Face Transformers Tutorial (Spring '24)\n",
        "Original Author: Ben Newman\n",
        "\n",
        "Thanks to Anna Goldie for feedback!\n",
        "\n",
        "This notebook will give an introduction to the Hugging Face Transformers Python library and some common patterns that you can use to take advantage of it. It is most useful for using or fine-tuning pretrained transformer models for your projects.\n",
        "\n",
        "\n",
        "Hugging Face provides access to models (both the code that implements them and their pre-trained weights, including latest LLMs like Llama3, DBRX, etc), model-specific tokenizers, as well as pipelines for common NLP tasks, and datasets and metrics in a separate `datasets` package. It has implementations in PyTorch, Tensorflow, and Flax (though we'll be using the PyTorch versions here!)\n",
        "\n",
        "\n",
        "We're going to go through a few use cases:\n",
        "* Overview of Tokenizers and Models\n",
        "* Finetuning - for your own task. We'll use a sentiment-classification example.\n",
        "\n",
        "\n",
        "Professor spoke about a few main project types in last Thursday's lecture:\n",
        "1. Applying an existing pre-trained model to a new application or task and explore how to approach/solve it\n",
        "2. Implementing a new or complex neural architecture and demonstrate its performance on some data\n",
        "3. Analyzing the behavior of a model: how it represents linguistic knowledge or what kinds of phenomena it can handle or errors that it makes\n",
        "\n",
        "Of these, `transformers` will be the most help for (1) and for (3). (2) involves a bit of learning curve but if you master it, you will find it very convenient to design a model based on existing ones provided by Huggingface. We won't be covering it here and please refer to [this example](https://huggingface.co/docs/transformers/en/custom_models).\n",
        "\n",
        "\n",
        "\n",
        "Here are additional resources introducing the library that were used to make this tutorial:\n",
        "\n",
        "* [Hugging Face Docs](https://huggingface.co/docs/transformers/index)\n",
        "  * Clear documentation\n",
        "  * Tutorials, walk-throughs, and example notebooks\n",
        "  * List of available models\n",
        "* [Hugging Face Course](https://huggingface.co/course/)\n",
        "* [Hugging Face Examples](https://github.com/huggingface/transformers/tree/main/examples/pytorch) You can find very similar code structures accross very different downstream tasks/models using Huggingface.\n",
        "* [Hugging Face O'Reilly Book](https://www.oreilly.com/library/view/natural-language-processing/9781098103231/)\n",
        "    * Students have FREE access through the Stanford Library!\n",
        "\n"
      ],
      "id": "sKB26q-4-X8r"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "9EhWoZef-X8u",
        "outputId": "2cc17953-33a8-4628-c951-40b07dc0b0e0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install transformers\n",
        "!pip install datasets\n",
        "!pip install accelerate"
      ],
      "id": "9EhWoZef-X8u"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q9th7mpc-X8v"
      },
      "outputs": [],
      "source": [
        "from collections import defaultdict, Counter\n",
        "import json\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import torch\n",
        "\n",
        "def print_encoding(model_inputs, indent=4):\n",
        "    indent_str = \" \" * indent\n",
        "    print(\"{\")\n",
        "    for k, v in model_inputs.items():\n",
        "        print(indent_str + k + \":\")\n",
        "        print(indent_str + indent_str + str(v))\n",
        "    print(\"}\")"
      ],
      "id": "Q9th7mpc-X8v"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qmXezUMg2idv"
      },
      "source": [
        "## Part 0: Common Pattern for using Hugging Face Transformers\n",
        "\n",
        "We're going to start off with a common usage pattern for Hugging Face Transformers, using the example of Sentiment Analysis.\n",
        "\n",
        "First, find a model on [the hub](https://huggingface.co/models). Anyone can upload their model for other people to use. (I'm using a sentiment analysis model from [this paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3489963)).\n",
        "\n",
        "Then, there are two objects that need to be initialized - a **tokenizer**, and a **model**\n",
        "\n",
        "* Tokenizer converts strings to lists of vocabulary ids that the model requires\n",
        "* Model takes the vocabulary ids and produces a prediction"
      ],
      "id": "qmXezUMg2idv"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ySLmJ0Z-oD35"
      },
      "source": [
        "![full_nlp_pipeline.png](https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter2/full_nlp_pipeline.svg)\n",
        "From [https://huggingface.co/course/chapter2/2?fw=pt](https://huggingface.co/course/chapter2/2?fw=pt)"
      ],
      "id": "ySLmJ0Z-oD35"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mcsii_O42Z8Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5bba7776-ee44-4d8b-9467-e32def806389"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
        "\n",
        "# Initialize the tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"siebert/sentiment-roberta-large-english\")\n",
        "# Initialize the model\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\"siebert/sentiment-roberta-large-english\")"
      ],
      "id": "Mcsii_O42Z8Q"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kT_zeWRBoD36",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d54a618e-14dc-4c0e-ef03-5472889f88c6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input:\n",
            "I'm excited to learn about Hugging Face Transformers!\n",
            "\n",
            "Tokenized Inputs:\n",
            "{\n",
            "    input_ids:\n",
            "        tensor([[    0,   100,   437,  2283,     7,  1532,    59, 30581,  3923, 12346,\n",
            "         34379,   328,     2]])\n",
            "    attention_mask:\n",
            "        tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\n",
            "}\n",
            "\n",
            "Model Outputs:\n",
            "SequenceClassifierOutput(loss=None, logits=tensor([[-3.7605,  2.9262]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)\n",
            "\n",
            "The prediction is POSITIVE\n"
          ]
        }
      ],
      "source": [
        "inputs = \"I'm excited to learn about Hugging Face Transformers!\"\n",
        "tokenized_inputs = tokenizer(inputs, return_tensors=\"pt\")\n",
        "outputs = model(**tokenized_inputs)\n",
        "\n",
        "labels = ['NEGATIVE', 'POSITIVE']\n",
        "prediction = torch.argmax(outputs.logits)\n",
        "\n",
        "\n",
        "print(\"Input:\")\n",
        "print(inputs)\n",
        "print()\n",
        "print(\"Tokenized Inputs:\")\n",
        "print_encoding(tokenized_inputs)\n",
        "print()\n",
        "print(\"Model Outputs:\")\n",
        "print(outputs)\n",
        "print()\n",
        "print(f\"The prediction is {labels[prediction]}\")"
      ],
      "id": "kT_zeWRBoD36"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a7jvH9haoD37"
      },
      "source": [
        "### 0.1 Tokenizers"
      ],
      "id": "a7jvH9haoD37"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "43FLbwgz-X83"
      },
      "source": [
        "Pretrained models are implemented along with **tokenizers** that are used to preprocess their inputs. The tokenizers take raw strings or list of strings and output what are effectively dictionaries that contain the the model inputs.\n",
        "\n",
        "\n",
        "You can access tokenizers either with the Tokenizer class specific to the model you want to use (here DistilBERT), or with the AutoTokenizer class.\n",
        "Fast Tokenizers are written in Rust, while their slow versions are written in Python."
      ],
      "id": "43FLbwgz-X83"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Pu6L0lWG-X83",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "546092b8-a3a2-4fc6-ed1d-f06e3f5f86ff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DistilBertTokenizer(name_or_path='distilbert/distilbert-base-cased', vocab_size=28996, model_max_length=1000000000000000019884624838656, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'}, clean_up_tokenization_spaces=True),  added_tokens_decoder={\n",
            "\t0: AddedToken(\"[PAD]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
            "\t100: AddedToken(\"[UNK]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
            "\t101: AddedToken(\"[CLS]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
            "\t102: AddedToken(\"[SEP]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
            "\t103: AddedToken(\"[MASK]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
            "}\n",
            "DistilBertTokenizerFast(name_or_path='distilbert/distilbert-base-cased', vocab_size=28996, model_max_length=1000000000000000019884624838656, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'}, clean_up_tokenization_spaces=True),  added_tokens_decoder={\n",
            "\t0: AddedToken(\"[PAD]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
            "\t100: AddedToken(\"[UNK]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
            "\t101: AddedToken(\"[CLS]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
            "\t102: AddedToken(\"[SEP]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
            "\t103: AddedToken(\"[MASK]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
            "}\n",
            "DistilBertTokenizerFast(name_or_path='distilbert/distilbert-base-cased', vocab_size=28996, model_max_length=1000000000000000019884624838656, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'}, clean_up_tokenization_spaces=True),  added_tokens_decoder={\n",
            "\t0: AddedToken(\"[PAD]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
            "\t100: AddedToken(\"[UNK]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
            "\t101: AddedToken(\"[CLS]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
            "\t102: AddedToken(\"[SEP]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
            "\t103: AddedToken(\"[MASK]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "from transformers import DistilBertTokenizer, DistilBertTokenizerFast, AutoTokenizer\n",
        "name = \"distilbert/distilbert-base-cased\"\n",
        "# name = \"user/name\" when loading from\n",
        "# name = local_path when using save_pretrained() method\n",
        "\n",
        "tokenizer = DistilBertTokenizer.from_pretrained(name)      # written in Python\n",
        "print(tokenizer)\n",
        "tokenizer = DistilBertTokenizerFast.from_pretrained(name)  # written in Rust\n",
        "print(tokenizer)\n",
        "tokenizer = AutoTokenizer.from_pretrained(name) # convenient! Defaults to Fast\n",
        "print(tokenizer)"
      ],
      "id": "Pu6L0lWG-X83"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mUWigfZD1yIQ"
      },
      "id": "mUWigfZD1yIQ",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zrPzbBhR-X84",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc8c7486-6957-4304-fcce-0fbdcbcaf4f6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vanilla Tokenization\n",
            "{\n",
            "    input_ids:\n",
            "        [101, 20164, 10932, 10289, 25267, 1110, 1632, 106, 102]\n",
            "    attention_mask:\n",
            "        [1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
            "}\n",
            "\n",
            "[101, 20164, 10932, 10289, 25267, 1110, 1632, 106, 102]\n",
            "[101, 20164, 10932, 10289, 25267, 1110, 1632, 106, 102]\n"
          ]
        }
      ],
      "source": [
        "# This is how you call the tokenizer\n",
        "input_str = \"Hugging Face Transformers is great!\"\n",
        "tokenized_inputs = tokenizer(input_str) # https://huggingface.co/learn/nlp-course/en/chapter6/6\n",
        "\n",
        "\n",
        "print(\"Vanilla Tokenization\")\n",
        "print_encoding(tokenized_inputs)\n",
        "print()\n",
        "\n",
        "# Two ways to access:\n",
        "print(tokenized_inputs.input_ids)\n",
        "print(tokenized_inputs[\"input_ids\"])"
      ],
      "id": "zrPzbBhR-X84"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E_8C6L2G-X85",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8b0bd9ba-cc08-4ecc-86a4-e686c3dbbfa1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "start:                 Hugging Face Transformers is great!\n",
            "tokenize:              ['Hu', '##gging', 'Face', 'Transformers', 'is', 'great', '!']\n",
            "convert_tokens_to_ids: [20164, 10932, 10289, 25267, 1110, 1632, 106]\n",
            "add special tokens:    [101, 20164, 10932, 10289, 25267, 1110, 1632, 106, 102]\n",
            "--------\n",
            "decode:                [CLS] Hugging Face Transformers is great! [SEP]\n"
          ]
        }
      ],
      "source": [
        "cls = [tokenizer.cls_token_id]\n",
        "sep = [tokenizer.sep_token_id]\n",
        "\n",
        "# Tokenization happens in a few steps:\n",
        "input_tokens = tokenizer.tokenize(input_str)\n",
        "input_ids = tokenizer.convert_tokens_to_ids(input_tokens)\n",
        "input_ids_special_tokens = cls + input_ids + sep\n",
        "\n",
        "decoded_str = tokenizer.decode(input_ids_special_tokens)\n",
        "\n",
        "print(\"start:                \", input_str)\n",
        "print(\"tokenize:             \", input_tokens)\n",
        "print(\"convert_tokens_to_ids:\", input_ids)\n",
        "print(\"add special tokens:   \", input_ids_special_tokens)\n",
        "print(\"--------\")\n",
        "print(\"decode:               \", decoded_str)\n",
        "\n",
        "# NOTE that these steps don't create the attention mask or add the special characters"
      ],
      "id": "E_8C6L2G-X85"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tdoZ3EEU-X86",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d7aa737-db24-45aa-de6e-8a8699af3404"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hugging Face Transformers is great!\n",
            "-----\n",
            "Number of tokens: 9\n",
            "Ids: [101, 20164, 10932, 10289, 25267, 1110, 1632, 106, 102]\n",
            "Tokens: ['[CLS]', 'Hu', '##gging', 'Face', 'Transformers', 'is', 'great', '!', '[SEP]']\n",
            "Special tokens mask: [1, 0, 0, 0, 0, 0, 0, 0, 1]\n",
            "\n",
            "char_to_word gives the wordpiece of a character in the input\n",
            "For example, the 9th character of the string is 'F', and it's part of wordpiece 3, 'Face'\n"
          ]
        }
      ],
      "source": [
        "# For Fast Tokenizers, there's another option too:\n",
        "inputs = tokenizer._tokenizer.encode(input_str)\n",
        "\n",
        "print(input_str)\n",
        "print(\"-\"*5)\n",
        "print(f\"Number of tokens: {len(inputs)}\")\n",
        "print(f\"Ids: {inputs.ids}\")\n",
        "print(f\"Tokens: {inputs.tokens}\")\n",
        "print(f\"Special tokens mask: {inputs.special_tokens_mask}\")\n",
        "print()\n",
        "print(\"char_to_word gives the wordpiece of a character in the input\")\n",
        "char_idx = 8\n",
        "print(f\"For example, the {char_idx + 1}th character of the string is '{input_str[char_idx]}',\"+\\\n",
        "      f\" and it's part of wordpiece {inputs.char_to_token(char_idx)}, '{inputs.tokens[inputs.char_to_token(char_idx)]}'\")"
      ],
      "id": "tdoZ3EEU-X86"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vt5WV-6S-X87",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f2c8eba1-2b7e-49e9-ca90-f24bee18c226"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PyTorch Tensors:\n",
            "{\n",
            "    input_ids:\n",
            "        tensor([[  101, 20164, 10932, 10289, 25267,  1110,  1632,   106,   102]])\n",
            "    attention_mask:\n",
            "        tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1]])\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "# Other cool tricks:\n",
        "# The tokenizer can return pytorch tensors\n",
        "model_inputs = tokenizer(\"Hugging Face Transformers is great!\", return_tensors=\"pt\")\n",
        "print(\"PyTorch Tensors:\")\n",
        "print_encoding(model_inputs)"
      ],
      "id": "vt5WV-6S-X87"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HI3bAzpeoD3_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9902a9a7-602f-4f3f-c8c3-7b04f026c193"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pad token: [PAD] | Pad token id: 0\n",
            "Padding:\n",
            "{\n",
            "    input_ids:\n",
            "        tensor([[  101, 20164, 10932, 10289, 25267,  1110,  1632,   106,   102,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0],\n",
            "        [  101,  1109,  3613,  3058, 17594, 15457,  1166,  1103, 16688,  3676,\n",
            "           119,  1599,  1103,  3676,  1400,  1146,  1105,  1868,  1283,  1272,\n",
            "          1131,  1238,   112,   189,  1176, 17594,  1279,   119,   102]])\n",
            "    attention_mask:\n",
            "        tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "         0, 0, 0, 0, 0],\n",
            "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "         1, 1, 1, 1, 1]])\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "# You can pass multiple strings into the tokenizer and pad them as you need\n",
        "model_inputs = tokenizer([\"Hugging Face Transformers is great!\",\n",
        "                         \"The quick brown fox jumps over the lazy dog.\" +\\\n",
        "                         \"Then the dog got up and ran away because she didn't like foxes.\",\n",
        "                         ],\n",
        "                         return_tensors=\"pt\",\n",
        "                         padding=True,\n",
        "                         truncation=True)\n",
        "print(f\"Pad token: {tokenizer.pad_token} | Pad token id: {tokenizer.pad_token_id}\")\n",
        "print(\"Padding:\")\n",
        "print_encoding(model_inputs)"
      ],
      "id": "HI3bAzpeoD3_"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": false,
        "id": "iSZat-nkoD3_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "724a6d4b-777e-4438-ee03-d6f8862bbefb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Batch Decode:\n",
            "['[CLS] Hugging Face Transformers is great! [SEP] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]', \"[CLS] The quick brown fox jumps over the lazy dog. Then the dog got up and ran away because she didn't like foxes. [SEP]\"]\n",
            "\n",
            "Batch Decode: (no special characters)\n",
            "['Hugging Face Transformers is great!', \"The quick brown fox jumps over the lazy dog. Then the dog got up and ran away because she didn't like foxes.\"]\n"
          ]
        }
      ],
      "source": [
        "# You can also decode a whole batch at once:\n",
        "print(\"Batch Decode:\")\n",
        "print(tokenizer.batch_decode(model_inputs.input_ids))\n",
        "print()\n",
        "print(\"Batch Decode: (no special characters)\")\n",
        "print(tokenizer.batch_decode(model_inputs.input_ids, skip_special_tokens=True))"
      ],
      "id": "iSZat-nkoD3_"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JmZNLz_noD4A"
      },
      "source": [
        "For more information about tokenizers, you can look at:\n",
        "[Hugging Face Transformers Docs](https://huggingface.co/docs/transformers/main_classes/tokenizer) and the [Hugging Face Tokenizers Library](https://huggingface.co/docs/tokenizers/python/latest/quicktour.html) (For the Fast Tokenizers). The Tokenizers Library even lets you train your own tokenizers!"
      ],
      "id": "JmZNLz_noD4A"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6juLjnNt-X87"
      },
      "source": [
        "### 0.2 Models\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "Initializing models is very similar to initializing tokenizers. You can either use the model class specific to your model or you can use an AutoModel class. I tend to prefer AutoModel, especially when I want to compare models, because it's easy to specify the models as strings.\n",
        "\n",
        "While most of the pretrained transformers have similar architecture, if you there are additional weights, called \"heads\" that you have to train if you're doing sequence classification, question answering, or some other task. Hugging Face automatically sets up the architecture you need when you specify the model class. For example, we are doing sentiment analysis, so we are going to use `DistilBertForSequenceClassification`. If we were going to continue training DistilBERT on its masked-language modeling training objective, we would use `DistilBertForMaskedLM`, and if we just wanted the model's representations, maybe for our own downstream task, we could just use `DistilBertModel`.\n",
        "\n",
        "\n",
        "Here's a stylized picture of a model recreated from one found here: [https://huggingface.co/course/chapter2/2?fw=pt](https://huggingface.co/course/chapter2/2?fw=pt).\n",
        "![model_illustration.png](https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter2/transformer_and_head.svg)\n",
        "\n",
        "\n",
        "Here are some examples.\n",
        "```\n",
        "*Model\n",
        "*ForMaskedLM\n",
        "*ForSequenceClassification\n",
        "*ForTokenClassification\n",
        "*ForQuestionAnswering\n",
        "*ForMultipleChoice\n",
        "...\n",
        "```\n",
        "where `*` can be `AutoModel` or a specific pretrained model (e.g. `DistilBert`)\n",
        "\n",
        "\n",
        "There are three types of models:\n",
        "* Encoders (e.g. BERT)\n",
        "* Decoders (e.g. GPT2)\n",
        "* Encoder-Decoder models (e.g. BART or T5)\n",
        "\n",
        "The task-specific classes you have available depend on what type of model you're dealing with.\n",
        "\n",
        "\n",
        "A full list of choices are available in the [docs](https://huggingface.co/docs/transformers/model_doc/auto). Note that not all models are compatible with all model architectures, for example DistilBERT is not compatible with the Seq2Seq models because it only consists of an encoder.\n"
      ],
      "id": "6juLjnNt-X87"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RXm1K2sF-X88",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "87db5173-398c-48c7-9df1-476144504613"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading base model\n",
            "Loading classification model from base model's checkpoint\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\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, DistilBertForSequenceClassification, DistilBertModel\n",
        "print('Loading base model')\n",
        "base_model = DistilBertModel.from_pretrained('distilbert-base-cased')\n",
        "print(\"Loading classification model from base model's checkpoint\")\n",
        "model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-cased', num_labels=2)\n",
        "model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-cased', num_labels=2)\n"
      ],
      "id": "RXm1K2sF-X88"
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can also initialize with random weights"
      ],
      "metadata": {
        "id": "v_5IMvUvgEWU"
      },
      "id": "v_5IMvUvgEWU"
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DistilBertConfig, DistilBertModel\n",
        "\n",
        "# Initializing a DistilBERT configuration\n",
        "configuration = DistilBertConfig()\n",
        "configuration.num_labels=2\n",
        "# Initializing a model (with random weights) from the configuration\n",
        "model = DistilBertForSequenceClassification(configuration)\n",
        "\n",
        "# Accessing the model configuration\n",
        "configuration = model.config"
      ],
      "metadata": {
        "id": "IkqSkIhKgIrZ"
      },
      "id": "IkqSkIhKgIrZ",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1opFV7Vi-X88"
      },
      "source": [
        "We get a warning here because the sequence classification parameters haven't been trained yet.\n",
        "\n",
        "Passing inputs to the model is super easy. They take inputs as keyword arguments"
      ],
      "id": "1opFV7Vi-X88"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TDZ72k-U-X89",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52285d1b-6d62-4d2d-827e-df23c61bbf8f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': tensor([[  101, 20164, 10932, 10289, 25267,  1110,  1632,   106,   102]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1]])}\n",
            "\n",
            "SequenceClassifierOutput(loss=None, logits=tensor([[0.0368, 0.0659]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)\n",
            "\n",
            "Distribution over labels: tensor([[0.4927, 0.5073]], grad_fn=<SoftmaxBackward0>)\n"
          ]
        }
      ],
      "source": [
        "model_inputs = tokenizer(input_str, return_tensors=\"pt\")\n",
        "\n",
        "# Option 1\n",
        "model_outputs = model(input_ids=model_inputs.input_ids, attention_mask=model_inputs.attention_mask)\n",
        "\n",
        "# Option 2 - the keys of the dictionary the tokenizer returns are the same as the keyword arguments\n",
        "#            the model expects\n",
        "\n",
        "# f({k1: v1, k2: v2}) = f(k1=v1, k2=v2)\n",
        "\n",
        "model_outputs = model(**model_inputs)\n",
        "\n",
        "print(model_inputs)\n",
        "print()\n",
        "print(model_outputs)\n",
        "print()\n",
        "print(f\"Distribution over labels: {torch.softmax(model_outputs.logits, dim=1)}\")"
      ],
      "id": "TDZ72k-U-X89"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oRRqhVUloD4C"
      },
      "source": [
        "If you notice, it's a bit weird that we have two classes for a binary classification task - you could easily have a single class and just choose a threshold. It's like this because of how huggingface models calculate the loss. This will increase the number of parameters we have, but shouldn't otherwise affect performance."
      ],
      "id": "oRRqhVUloD4C"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rvie4gYD-X8-"
      },
      "source": [
        "These models are just Pytorch Modules! You can can calculate the loss with your `loss_func` and call `loss.backward`. You can use any of the optimizers or learning rate schedulers that you used"
      ],
      "id": "rvie4gYD-X8-"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Irxo7sDboD4C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "36e02d3d-e69b-46e0-c747-461bde1a60d5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(0.6787, grad_fn=<NllLossBackward0>)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('distilbert.embeddings.word_embeddings.weight',\n",
              " Parameter containing:\n",
              " tensor([[-2.5130e-02, -3.3044e-02, -2.4396e-03,  ..., -1.0848e-02,\n",
              "          -4.6824e-02, -9.4855e-03],\n",
              "         [-4.8244e-03, -2.1486e-02, -8.7145e-03,  ..., -2.6029e-02,\n",
              "          -3.7862e-02, -2.4103e-02],\n",
              "         [-1.6531e-02, -1.7862e-02,  1.0596e-03,  ..., -1.6371e-02,\n",
              "          -3.5670e-02, -3.1419e-02],\n",
              "         ...,\n",
              "         [-9.6466e-03,  1.4814e-02, -2.9182e-02,  ..., -3.7873e-02,\n",
              "          -4.6263e-02, -1.6803e-02],\n",
              "         [-1.3170e-02,  6.5378e-05, -3.7222e-02,  ..., -4.3558e-02,\n",
              "          -1.1252e-02, -2.2152e-02],\n",
              "         [ 1.1905e-02, -2.3293e-02, -2.2506e-02,  ..., -2.7136e-02,\n",
              "          -4.3556e-02,  1.0529e-04]], requires_grad=True))"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "# You can calculate the loss like normal\n",
        "label = torch.tensor([1])\n",
        "loss = torch.nn.functional.cross_entropy(model_outputs.logits, label)\n",
        "print(loss)\n",
        "loss.backward()\n",
        "\n",
        "# You can get the parameters\n",
        "list(model.named_parameters())[0]"
      ],
      "id": "Irxo7sDboD4C"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mpHeG1zDoD4D"
      },
      "source": [
        "Hugging Face provides an additional easy way to calculate the loss as well:"
      ],
      "id": "mpHeG1zDoD4D"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S148gCyG-X8-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2d3d761-ecce-4d96-abf3-acedc2c460e3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SequenceClassifierOutput(loss=tensor(0.6787, grad_fn=<NllLossBackward0>), logits=tensor([[0.0368, 0.0659]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)\n",
            "\n",
            "Model predictions: POSITIVE\n"
          ]
        }
      ],
      "source": [
        "# To calculate the loss, we need to pass in a label:\n",
        "model_inputs = tokenizer(input_str, return_tensors=\"pt\")\n",
        "\n",
        "labels = ['NEGATIVE', 'POSITIVE']\n",
        "model_inputs['labels'] = torch.tensor([1])\n",
        "\n",
        "model_outputs = model(**model_inputs)\n",
        "\n",
        "\n",
        "print(model_outputs)\n",
        "print()\n",
        "print(f\"Model predictions: {labels[model_outputs.logits.argmax()]}\")"
      ],
      "id": "S148gCyG-X8-"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Y6E3IxzoD4E"
      },
      "source": [
        "One final note - you can get the hidden states and attention weights from the models really easily. This is particularly helpful if you're working on an analysis project. (For example, see [What does BERT look at?](https://arxiv.org/abs/1906.04341))."
      ],
      "id": "7Y6E3IxzoD4E"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5WzqhpquoD4E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b8e84ba-4ffd-4a8f-920a-843c14fdcd70"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hidden state size (per layer):   torch.Size([1, 9, 768])\n",
            "Attention head size (per layer): torch.Size([1, 12, 9, 9])\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModel\n",
        "\n",
        "model = AutoModel.from_pretrained(\"distilbert-base-cased\", output_attentions=True, output_hidden_states=True)\n",
        "model.eval()\n",
        "\n",
        "model_inputs = tokenizer(input_str, return_tensors=\"pt\")\n",
        "with torch.no_grad():\n",
        "    model_output = model(**model_inputs)\n",
        "\n",
        "\n",
        "print(\"Hidden state size (per layer):  \", model_output.hidden_states[0].shape)\n",
        "print(\"Attention head size (per layer):\", model_output.attentions[0].shape)     # (layer, batch, query_word_idx, key_word_idxs)\n",
        "                                                                               # y-axis is query, x-axis is key\n",
        "# print(model_output)"
      ],
      "id": "5WzqhpquoD4E"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SH_MAK-soD4F",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 693
        },
        "outputId": "b5a45a91-b019-4b2f-af37-366ca60dc058"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['[CLS]', 'Hu', '##gging', 'Face', 'Transformers', 'is', 'great', '!', '[SEP]']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3700x2100 with 72 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokens = tokenizer.convert_ids_to_tokens(model_inputs.input_ids[0])\n",
        "print(tokens)\n",
        "\n",
        "\n",
        "n_layers = len(model_output.attentions)\n",
        "n_heads = len(model_output.attentions[0][0])\n",
        "fig, axes = plt.subplots(6, 12)\n",
        "fig.set_size_inches(18.5*2, 10.5*2)\n",
        "for layer in range(n_layers):\n",
        "    for i in range(n_heads):\n",
        "        axes[layer, i].imshow(model_output.attentions[layer][0, i])\n",
        "        axes[layer][i].set_xticks(list(range(9)))\n",
        "        axes[layer][i].set_xticklabels(labels=tokens, rotation=\"vertical\")\n",
        "        axes[layer][i].set_yticks(list(range(9)))\n",
        "        axes[layer][i].set_yticklabels(labels=tokens)\n",
        "\n",
        "        if layer == 5:\n",
        "            axes[layer, i].set(xlabel=f\"head={i}\")\n",
        "        if i == 0:\n",
        "            axes[layer, i].set(ylabel=f\"layer={layer}\")\n",
        "\n",
        "plt.subplots_adjust(wspace=0.3)\n",
        "plt.show()"
      ],
      "id": "SH_MAK-soD4F"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uumcErs2-X80"
      },
      "source": [
        "## Part 1: Finetuning\n",
        "\n",
        "For your projects, you are much more likely to want to finetune a pretrained model. This is a little bit more involved, but is still quite easy."
      ],
      "id": "uumcErs2-X80"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WDdGp4Ua-X81"
      },
      "source": [
        "### 2.1 Loading in a dataset\n",
        "\n",
        "In addition to having models, the [the hub](https://huggingface.co/datasets) also has datasets."
      ],
      "id": "WDdGp4Ua-X81"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OTsW-Wwi-X81",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "20d75c4d-702f-4231-ef4f-44534fe051d3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 25000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 25000\n",
              "    })\n",
              "    unsupervised: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 50000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "from datasets import load_dataset, DatasetDict\n",
        "\n",
        "\n",
        "\n",
        "# DataLoader(zip(list1, list2))\n",
        "dataset_name = \"stanfordnlp/imdb\"\n",
        "\n",
        "imdb_dataset = load_dataset(dataset_name)\n",
        "\n",
        "\n",
        "# Just take the first 50 tokens for speed/running on cpu\n",
        "def truncate(example):\n",
        "    return {\n",
        "        'text': \" \".join(example['text'].split()[:50]),\n",
        "        'label': example['label']\n",
        "    }\n",
        "\n",
        "imdb_dataset"
      ],
      "id": "OTsW-Wwi-X81"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vCaX-gNo0OEV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d043c74-6e73-405f-91a5-823d98a0a973"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 128\n",
              "    })\n",
              "    val: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 32\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "\n",
        "# Take 128 random examples for train and 32 validation\n",
        "small_imdb_dataset = DatasetDict(\n",
        "    train=imdb_dataset['train'].shuffle(seed=1111).select(range(128)).map(truncate),\n",
        "    val=imdb_dataset['train'].shuffle(seed=1111).select(range(128, 160)).map(truncate),\n",
        ")\n",
        "small_imdb_dataset"
      ],
      "id": "vCaX-gNo0OEV"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bBS4c44A-X82",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d6cf0d8-3a62-43a6-87f2-bc2d039b44a1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': [\"Probably Jackie Chan's best film in the 1980s, and the one that put him on the map. The scale of this self-directed police drama is evident from the opening and closing scenes, during which a squatters' village and shopping mall are demolished. There are, clearly, differences between the original Chinese\",\n",
              "  'A wonderful movie! Anyone growing up in an Italian family will definitely see themselves in these characters. A good family movie with sadness, humor, and very good acting from all. You will enjoy this movie!! We need more like it.',\n",
              "  'HORRENDOUS! Avoid like the plague. I would rate this in the top 10 worst movies ever. Special effects, acting, mood, sound, etc. appear to be done by day care students...wait, I have seen programs better than this. Opens like a soft porn show with a blurred nude female doing a',\n",
              "  'And I absolutely adore Isabelle Blais!!! She was so cute in this movie, and far different from her role in \"Quebec-Montreal\" where she was more like a man-eater. I think she should have been nominated for a Jutra. I mean, Syvlie Moreau was good, but Isabelle was far superior, IMO.',\n",
              "  'Must confess to having seen a few howlers in my time, but this one is up there with the worst of them. Plot troubling to follow. Sex and violence thrown in to disorient and distract from the really poorly put together film.<br /><br />I can only imagine that the cast',\n",
              "  \"I pity people calling kamal hassan 'ulaganaayakan' maybe for them ulagam is tollywood ! comeon guys..this movie is a thriller without thrill..<br /><br />come out of your ulagam and just watch some high class thrillers like The Usual Suspects or even The Silence of the Lambs.<br /><br />technically good but\",\n",
              "  'I remember my parents not understanding Saturday Night Live when I was 15. They also did not understand Rock n Roll and many other things. Now that I am approaching their age, I still remember, and find I understand many of the things my kids love. But this is pathetic.',\n",
              "  'This animated movie is a masterpiece! The narration, music, animation, and storyline where all remarkable. My girlfriend and I saw it again for a second time and we got more insight from it. We invited a couple friends to see Spirit with us and they really enjoyed it a lot.',\n",
              "  'I vaugely recall seeing this when I was 3 years old, then my parents accidentally taped over all but a few seconds of it with some other cartoon. Then I was about 8 or 9 years old when I rediscovered it and since I was then able to comprehend things',\n",
              "  'I, like many people, saw this film in the theatre when it first came out in \\'97. It was a below average film at best, defiantly not the \"masterpiece\" that all these \"Titanic\" fanboys like to make it out as. First off, DiCaprio is a terrible actor no matter which'],\n",
              " 'label': [1, 1, 0, 1, 0, 0, 0, 1, 1, 0]}"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "small_imdb_dataset['train'][:10]"
      ],
      "id": "bBS4c44A-X82"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3bjqop3N-X8_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "65a533fb60804956b220793116238e88",
            "a0cd7a1e5bee4d9a9fe55cafa03e972b",
            "808a343a24cd4fa19ce8942e366b0d81",
            "f591cd9700af440ebe81fd24d103d7f0",
            "e9eecd62333840d2a937a10b616985fb",
            "83f278f11f6d46c993f9605518451a31",
            "3164bb6d64394c569b434058b83ec0cb",
            "ab8c5db7bb4246d6bda6b1e900e50600",
            "5d8cd6e7192140cdbf0b20af03d17c4a",
            "62ab925b526942f0bc34c52ba2457da5",
            "cdadcb68bdd04d34b120c347822787aa"
          ]
        },
        "outputId": "69eaa47f-23c6-4f86-d2f9-b6f08762bb64"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/32 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "65a533fb60804956b220793116238e88"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Prepare the dataset - this tokenizes the dataset in batches of 16 examples.\n",
        "small_tokenized_dataset = small_imdb_dataset.map(\n",
        "    lambda example: tokenizer(example['text'], padding=True, truncation=True), # https://huggingface.co/docs/transformers/pad_truncation\n",
        "    batched=True,\n",
        "    batch_size=16\n",
        ")\n",
        "\n",
        "small_tokenized_dataset = small_tokenized_dataset.remove_columns([\"text\"])\n",
        "small_tokenized_dataset = small_tokenized_dataset.rename_column(\"label\", \"labels\")\n",
        "small_tokenized_dataset.set_format(\"torch\")"
      ],
      "id": "3bjqop3N-X8_"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "450eUYlf-X8_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0881225b-8cce-467e-fd90-fa75b0bca6c7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'labels': tensor([1, 1]),\n",
              " 'input_ids': tensor([[  101, 10109,  9662, 10185,   112,   188,  1436,  1273,  1107,  1103,\n",
              "           3011,   117,  1105,  1103,  1141,  1115,  1508,  1140,  1113,  1103,\n",
              "           4520,   119,  1109,  3418,  1104,  1142,  2191,   118,  2002,  2021,\n",
              "           3362,  1110, 10238,  1121,  1103,  2280,  1105,  5134,  4429,   117,\n",
              "           1219,  1134,   170,  4816,  6718, 18899,   112,  1491,  1105,  6001,\n",
              "           8796,  1132,  6515,   119,  1247,  1132,   117,  3817,   117,  5408,\n",
              "           1206,  1103,  1560,  1922,   102,     0,     0,     0,     0,     0,\n",
              "              0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "              0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "              0,     0],\n",
              "         [  101,   138,  7310,  2523,   106, 15859,  2898,  1146,  1107,  1126,\n",
              "           2169,  1266,  1209,  5397,  1267,  2310,  1107,  1292,  2650,   119,\n",
              "            138,  1363,  1266,  2523,  1114, 12928,   117,  8594,   117,  1105,\n",
              "           1304,  1363,  3176,  1121,  1155,   119,  1192,  1209,  5548,  1142,\n",
              "           2523,   106,   106,  1284,  1444,  1167,  1176,  1122,   119,   102,\n",
              "              0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "              0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "              0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "              0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "              0,     0]]),\n",
              " 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
              "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "         [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "small_tokenized_dataset['train'][0:2]"
      ],
      "id": "450eUYlf-X8_"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q3e7_htt-X8_"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "train_dataloader = DataLoader(small_tokenized_dataset['train'], batch_size=16)\n",
        "eval_dataloader = DataLoader(small_tokenized_dataset['val'], batch_size=16)"
      ],
      "id": "Q3e7_htt-X8_"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qRm6Tw_z-X8-"
      },
      "source": [
        "### 2.2 Training\n",
        "\n",
        "To train your models, you can just use the same kind of training loop that you would use in Pytorch. Hugging Face models are also `torch.nn.Module`s so backpropagation happens the same way and you can even use the same optimizers. Hugging Face also includes optimizers and learning rate schedules that were used to train Transformer models, so you can use these too.\n",
        "\n",
        "For optimization, we're using the AdamW Optimizer, which is almost identical to Adam except it also includes weight decay.\n",
        "And we're using a linear learning rate scheduler, which reduces the learning rate a little bit after each training step over the course of training.\n",
        "\n",
        "There are other optimizers and learning rate schedulers you can use, but these are the default. If you want to explore, you can look at the ones [Hugging Face offers](https://huggingface.co/docs/transformers/main_classes/optimizer_schedules#schedules), the ones available through [Pytorch](https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate) (e.g. [ReduceLROnPlateau](https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.ReduceLROnPlateau.html), which only decreases the learning rate when the validation loss stops decreasing), or write your own (like the one in Assignment 4)."
      ],
      "id": "qRm6Tw_z-X8-"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A7CvjTIv-X9A",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173,
          "referenced_widgets": [
            "7439178f023b4baf99dbc5af2c0685f5",
            "6d708b3b1e8c4e56b82d0704463f80c0",
            "d4734564984c4cc090879870e909a8e0",
            "9059b94266944ccc9fe3fd3d1fe19ab5",
            "673080d7448948f8ba0358d1e1f6efb4",
            "6872dbb410804e42aae532de6857e4c8",
            "8c89cfc617594a1fbb673ca305cc123f",
            "fd643ce0c2ab462aa8aaa236b8ef4688",
            "8377ee4797e7430388e841daea16f252",
            "9c65b39e6e614695885b546cf79c5c1b",
            "fd6d0fab8cce44e79d4e177273d26840"
          ]
        },
        "outputId": "12544eb7-0ee8-4ae0-a0fc-0a1f9cae6276"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:521: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/8 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7439178f023b4baf99dbc5af2c0685f5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation loss: 1.0309011936187744\n",
            "Saving checkpoint!\n"
          ]
        }
      ],
      "source": [
        "from transformers import AdamW, get_linear_schedule_with_warmup\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "\n",
        "model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-cased', num_labels=2)\n",
        "\n",
        "num_epochs = 1\n",
        "num_training_steps = len(train_dataloader)\n",
        "optimizer = AdamW(model.parameters(), lr=5e-5, weight_decay=0.01)\n",
        "lr_scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=num_training_steps)\n",
        "\n",
        "best_val_loss = float(\"inf\")\n",
        "progress_bar = tqdm(range(num_training_steps))\n",
        "for epoch in range(num_epochs):\n",
        "    # training\n",
        "    model.train()\n",
        "    for batch_i, batch in enumerate(train_dataloader):\n",
        "\n",
        "        # batch = ([text1, text2], [0, 1])\n",
        "\n",
        "        output = model(**batch)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        output.loss.backward()\n",
        "        optimizer.step()\n",
        "        lr_scheduler.step()\n",
        "        progress_bar.update(1)\n",
        "\n",
        "    # validation\n",
        "    model.eval()\n",
        "    for batch_i, batch in enumerate(eval_dataloader):\n",
        "        with torch.no_grad():\n",
        "            output = model(**batch)\n",
        "        loss += output.loss\n",
        "\n",
        "    avg_val_loss = loss / len(eval_dataloader)\n",
        "    print(f\"Validation loss: {avg_val_loss}\")\n",
        "    if avg_val_loss < best_val_loss:\n",
        "        print(\"Saving checkpoint!\")\n",
        "        best_val_loss = avg_val_loss\n",
        "        # torch.save({\n",
        "        #     'epoch': epoch,\n",
        "        #     'model_state_dict': model.state_dict(),\n",
        "        #     'optimizer_state_dict': optimizer.state_dict(),\n",
        "        #     'val_loss': best_val_loss,\n",
        "        #     },\n",
        "        #     f\"checkpoints/epoch_{epoch}.pt\"\n",
        "        # )"
      ],
      "id": "A7CvjTIv-X9A"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2nz1v79ZbgHq"
      },
      "id": "2nz1v79ZbgHq",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch['input_ids'].max()"
      ],
      "metadata": {
        "id": "cl_JX8HKbVmb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6eab7f84-695e-4c00-8187-f4a5de45ee8f"
      },
      "id": "cl_JX8HKbVmb",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(27013)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YrfE9c04-X9A"
      },
      "source": [
        "While you can use PyTorch to train your models like we did in Assignment 4, Hugging Face offers a powerful `Trainer` class to handle most needs. I think it works pretty well, though there are some customizations I'd recommend."
      ],
      "id": "YrfE9c04-X9A"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fQDkqtT7-X9B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "ab935be7be504e3784de4d7f2cd7bff6",
            "c0a01a008c5e4caa915220ab3db7862e",
            "5494f014a89943fbaa52a3b760f7c9be",
            "0e00aedefc414e7a936d1e835ce4326a",
            "24764907a2b94fea9389650226a5bec2",
            "d2e472a543f94284a77fc019b9e7b9b0",
            "81d5e9b9b59648aa96bcdcf176e9e04f",
            "1ef0ff13297d43e2a8fbe07e8894a13f",
            "ed8c860c02f545469eb9e47d1f097443",
            "236ad067eca04d60abf593d0cddc8f2f",
            "e1681735974b44a2a7a6c92097239ceb"
          ]
        },
        "outputId": "5b8f4413-55cd-4ab1-a371-4c85a73936b7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/32 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ab935be7be504e3784de4d7f2cd7bff6"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "imdb_dataset = load_dataset(\"stanfordnlp/imdb\")\n",
        "\n",
        "small_imdb_dataset = DatasetDict(\n",
        "    train=imdb_dataset['train'].shuffle(seed=1111).select(range(128)).map(truncate),\n",
        "    val=imdb_dataset['train'].shuffle(seed=1111).select(range(128, 160)).map(truncate),\n",
        ")\n",
        "\n",
        "small_tokenized_dataset = small_imdb_dataset.map(\n",
        "    lambda example: tokenizer(example['text'], truncation=True),\n",
        "    batched=True,\n",
        "    batch_size=16\n",
        ")"
      ],
      "id": "fQDkqtT7-X9B"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RULukU5eoD4M"
      },
      "source": [
        "`TrainingArguments` specifies different training parameters like how often to evaluate and save model checkpoints, where to save them, etc. There are **many** aspects you can customize and it's worth checking them out [here](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments). Some things you can control include:\n",
        "* learning rate, weight decay, gradient clipping,\n",
        "* checkpointing, logging, and evaluation frequency\n",
        "* where you log to (default is tensorboard, but if you use WandB or MLFlow they have integrations)\n",
        "\n",
        "The `Trainer` actually performs the training. You can pass it the `TrainingArguments`, model, the datasets, tokenizer, optimizer, and even model checkpoints to resume training from. The `compute_metrics` function is called at the end of evaluation/validation to calculate evaluation metrics."
      ],
      "id": "RULukU5eoD4M"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FEmERCu_-X9B",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1d27ec03-52aa-4c3f-dbdd-0edbaaf6925b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\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 TrainingArguments, Trainer\n",
        "\n",
        "model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-cased', num_labels=2)\n",
        "\n",
        "arguments = TrainingArguments(\n",
        "    output_dir=\"sample_hf_trainer\",\n",
        "    per_device_train_batch_size=16,\n",
        "    per_device_eval_batch_size=16,\n",
        "    num_train_epochs=2,\n",
        "    evaluation_strategy=\"epoch\", # run validation at the end of each epoch\n",
        "    save_strategy=\"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    load_best_model_at_end=True,\n",
        "    seed=224\n",
        ")\n",
        "\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    \"\"\"Called at the end of validation. Gives accuracy\"\"\"\n",
        "    logits, labels = eval_pred\n",
        "    predictions = np.argmax(logits, axis=-1)\n",
        "    # calculates the accuracy\n",
        "    return {\"accuracy\": np.mean(predictions == labels)}\n",
        "\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=arguments,\n",
        "    train_dataset=small_tokenized_dataset['train'],\n",
        "    eval_dataset=small_tokenized_dataset['val'], # change to test when you do your final evaluation!\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics\n",
        ")"
      ],
      "id": "FEmERCu_-X9B"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MbyK1W95-X9B"
      },
      "source": [
        "#### Callbacks: Logging and Early Stopping\n",
        "\n",
        "\n",
        "Hugging Face Transformers also allows you to write `Callbacks` if you want certain things to happen at different points during training (e.g. after evaluation or after an epoch has finished). For example, there is a callback for early stopping, and I usually write one for logging as well.\n",
        "\n",
        "For more information on callbacks see [here](https://huggingface.co/docs/transformers/main_classes/callback#transformers.TrainerCallback)."
      ],
      "id": "MbyK1W95-X9B"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vKO3TkAnoD4N"
      },
      "outputs": [],
      "source": [
        "from transformers import TrainerCallback, EarlyStoppingCallback\n",
        "\n",
        "class LoggingCallback(TrainerCallback):\n",
        "    def __init__(self, log_path):\n",
        "        self.log_path = log_path\n",
        "    # will call on_log on each logging step, specified by TrainerArguement. (i.e TrainerArguement.logginng_step)\n",
        "    def on_log(self, args, state, control, logs=None, **kwargs):\n",
        "        _ = logs.pop(\"total_flos\", None)\n",
        "        if state.is_local_process_zero:\n",
        "            with open(self.log_path, \"a\") as f:\n",
        "                f.write(json.dumps(logs) + \"\\n\")\n",
        "    # def on_epoch(...)\n",
        "\n",
        "\n",
        "trainer.add_callback(EarlyStoppingCallback(early_stopping_patience=1, early_stopping_threshold=0.0))\n",
        "trainer.add_callback(LoggingCallback(\"sample_hf_trainer/log.jsonl\"))"
      ],
      "id": "vKO3TkAnoD4N"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tunwonc2-X9C",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "outputId": "3d047df7-9cc8-4dd1-905e-c74080bf71f2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='16' max='16' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [16/16 00:12, Epoch 2/2]\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>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.690763</td>\n",
              "      <td>0.687500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.689398</td>\n",
              "      <td>0.656250</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=16, training_loss=0.6855354309082031, metrics={'train_runtime': 12.879, 'train_samples_per_second': 19.877, 'train_steps_per_second': 1.242, 'train_loss': 0.6855354309082031, 'epoch': 2.0})"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ],
      "source": [
        "# train the model\n",
        "trainer.train()"
      ],
      "id": "tunwonc2-X9C"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_Q185j9V-X9C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "c0c5a89f-1137-4139-e4a6-19f150e32343"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ],
      "source": [
        "# evaluating the model is very easy\n",
        "\n",
        "# results = trainer.evaluate()                           # just gets evaluation metrics\n",
        "results = trainer.predict(small_tokenized_dataset['val']) # also gives you predictions"
      ],
      "id": "_Q185j9V-X9C"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UJ0aGxeh-X9D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48c19364-5b9c-4601-fc36-5a629bf29b35"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PredictionOutput(predictions=array([[ 0.00131953,  0.00580434],\n",
              "       [ 0.03595451,  0.03271278],\n",
              "       [ 0.02868645, -0.02640614],\n",
              "       [ 0.01930159,  0.04312824],\n",
              "       [ 0.006476  ,  0.03497026],\n",
              "       [ 0.02455846, -0.03357419],\n",
              "       [ 0.03111352, -0.02113312],\n",
              "       [ 0.01838659, -0.02196582],\n",
              "       [-0.0029597 ,  0.03812057],\n",
              "       [ 0.0017085 ,  0.01325338],\n",
              "       [ 0.01675256,  0.02372436],\n",
              "       [ 0.05900048, -0.06402317],\n",
              "       [ 0.02501691, -0.00633026],\n",
              "       [ 0.03639492, -0.01839869],\n",
              "       [ 0.02455333,  0.01321811],\n",
              "       [ 0.06327322, -0.07626322],\n",
              "       [ 0.04167526, -0.06864192],\n",
              "       [ 0.03178786,  0.01866044],\n",
              "       [ 0.04430217, -0.02746258],\n",
              "       [ 0.00703178,  0.0555813 ],\n",
              "       [ 0.04485226, -0.03188433],\n",
              "       [ 0.00631561,  0.00044989],\n",
              "       [ 0.00466784,  0.03991104],\n",
              "       [ 0.04301776, -0.03423208],\n",
              "       [ 0.02306983,  0.03840486],\n",
              "       [ 0.01598634,  0.03111538],\n",
              "       [ 0.00623961,  0.00687651],\n",
              "       [ 0.03995002, -0.017272  ],\n",
              "       [-0.00964497,  0.02151189],\n",
              "       [ 0.03343549, -0.00809752],\n",
              "       [ 0.01264727,  0.04387456],\n",
              "       [ 0.0167619 ,  0.00577193]], dtype=float32), label_ids=array([1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0,\n",
              "       0, 1, 1, 0, 1, 1, 1, 0, 0, 1]), metrics={'test_loss': 0.6893976926803589, 'test_accuracy': 0.65625, 'test_runtime': 0.13, 'test_samples_per_second': 246.122, 'test_steps_per_second': 15.383})"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ],
      "source": [
        "results"
      ],
      "id": "UJ0aGxeh-X9D"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kSGsZo3xoD4O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "65d9c731-435c-4924-da02-cba2765cd58c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NEGATIVE\n"
          ]
        }
      ],
      "source": [
        "# To load our saved model, we can pass the path to the checkpoint into the `from_pretrained` method:\n",
        "test_str = \"I enjoyed the movie!\"\n",
        "\n",
        "finetuned_model = AutoModelForSequenceClassification.from_pretrained(\"sample_hf_trainer/checkpoint-8\")\n",
        "model_inputs = tokenizer(test_str, return_tensors=\"pt\")\n",
        "prediction = torch.argmax(finetuned_model(**model_inputs).logits)\n",
        "print([\"NEGATIVE\", \"POSITIVE\"][prediction])"
      ],
      "id": "kSGsZo3xoD4O"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MllSTgehoD4O"
      },
      "source": [
        "Included here are also some practical tips for fine-tuning:\n",
        "\n",
        "**Good default hyperparameters.** The hyperparameters you will depend on your task and dataset. You should do a hyperparameter search to find the best ones. That said, here are some good initial values for fine-tuning.\n",
        "* Epochs: {2, 3, 4} (larger amounts of data need fewer epochs)\n",
        "* Batch size (bigger is better: as large as you can make it)\n",
        "* Optimizer: AdamW\n",
        "* AdamW learning rate: {2e-5, 5e-5}\n",
        "* Learning rate scheduler: linear warm up for first {0, 100, 500} steps of training\n",
        "* weight_decay (l2 regularization): {0, 0.01, 0.1}\n",
        "\n",
        "You should monitor your validation loss to decide when you've found good hyperparameters."
      ],
      "id": "MllSTgehoD4O"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gsWGQfrm-X9D"
      },
      "source": [
        "There's a lot more that we can integrate into the Trainer to make it more useful including logging, saving model checkpoints, and more! You can even sub-class it to add your own personalized components. You can check out [this link](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Trainer) for more information about the Trainer."
      ],
      "id": "gsWGQfrm-X9D"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9nCrUosgoD4P"
      },
      "source": [
        "## Appendix 0: Generation\n",
        "\n",
        "In the example above we finetuned the model on a classification task, but you can also finetune models on generation tasks. The `generate` function makes it easy to generate from these models. For example."
      ],
      "id": "9nCrUosgoD4P"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QfQEV8EKoD4P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fbd14511-e845-46a4-f23d-e0b533e85efa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModelForCausalLM\n",
        "\n",
        "gpt2_tokenizer = AutoTokenizer.from_pretrained('gpt2')\n",
        "\n",
        "gpt2 = AutoModelForCausalLM.from_pretrained('distilgpt2')\n",
        "gpt2.config.pad_token_id = gpt2.config.eos_token_id  # Prevents warning during decoding"
      ],
      "id": "QfQEV8EKoD4P"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G5wo61xmoD4Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "05d6ce0c-624b-4bba-c371-e35377951568"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1) Once upon a time when I was trying to get a job, I started feeling like the company would end up making a living. I was starting to feel that if it wasn't for the staff, they would lose, and the other staff would find\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2) Once upon a time, we thought that the first and last names of each player were not related to each other. But I had a feeling that we'd never get to the moment of discovery before. And when I heard it, I felt I'd\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3) Once upon a time of chaos, a group of monsters fell on the field and a few soldiers entered the air. One of them was the man who had been waiting for reinforcements, and he didn't seem to want to be seen. He was carrying\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4) Once upon a time of need, one would be forced to commit the most serious crime.\n",
            "\n",
            "\n",
            "\n",
            "The problem isn't just about the number of dead and dying, it's also about the importance of ensuring that people who are not dead will\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5) Once upon a time of war, you are a soldier, and if there is anything that can be done, I think it's enough.\"\n",
            "\n",
            "\n",
            "The Great War\n",
            "It wasn't a long time before the Great War began. There was a\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6) Once upon a time in which the sun had risen, a large man of all stripes with red and blue stripes sat on a bench facing towards a throne, his head turned red and a little black, which was a familiar sight to me.\n",
            "\"\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7) Once upon a time of my life, the people were really interested in my life. I went in a certain way and didn't want to go into politics or politics, I always wanted to be a good writer. I always wanted to make movies,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8) Once upon a time of great sadness the day before I would have said goodbye to her.”\n",
            "I had seen her in her parents’ home before. It seemed like I was so old. The family was only seven. I had seen\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9) Once upon a time the question becomes \"Can the system allow me to run things without running the system?\" and \"Does that mean I can run things without running the system?\"\n",
            "\n",
            "\n",
            "\n",
            "That seems to me, the idea of the \"aut\n",
            "10) Once upon a time, there was an entire community who knew their future would be up and running. The city of Seattle's first full-blown municipal government became a full-blown government.\n",
            "\n",
            "\n",
            "In fact, it's not the only city\n"
          ]
        }
      ],
      "source": [
        "prompt = \"Once upon a time\"\n",
        "\n",
        "tokenized_prompt = gpt2_tokenizer(prompt, return_tensors=\"pt\")\n",
        "\n",
        "for i in range(10):\n",
        "    output = gpt2.generate(**tokenized_prompt,\n",
        "                  max_length=50,\n",
        "                  do_sample=True,\n",
        "                  top_p=0.9)\n",
        "\n",
        "    print(f\"{i + 1}) {gpt2_tokenizer.batch_decode(output)[0]}\")"
      ],
      "id": "G5wo61xmoD4Q"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QLAHLU4q9HYQ"
      },
      "source": [
        "## Appendix 1: Defining Custom Datasets\n",
        "\n",
        "There are a few ways to go about defining datasets, but I'm going to show an example using Pytorch Dataloaders. This example uses an encoder-decoder dataaset,the [E2E Dataset](https://arxiv.org/abs/1706.09254), which is maps structured information about restaurants to natural language descriptions."
      ],
      "id": "QLAHLU4q9HYQ"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MLqz11UioD4Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "outputId": "5754856e-288a-4d93-aecc-a0c14f581f1b"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "[Errno 2] No such file or directory: 'e2e-dataset/trainset.csv'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-36-392d469fa624>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdatasets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"e2e-dataset/trainset.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mcustom_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pandas\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m    910\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    911\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 912\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    575\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    576\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 577\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    578\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1406\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1407\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1409\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1659\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1660\u001b[0m                     \u001b[0mmode\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"b\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1661\u001b[0;31m             self.handles = get_handle(\n\u001b[0m\u001b[1;32m   1662\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1663\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    857\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 859\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    860\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    861\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'e2e-dataset/trainset.csv'"
          ]
        }
      ],
      "source": [
        "# Option 1: Load into Hugging Face Datasets\n",
        "\n",
        "import pandas as pd\n",
        "from datasets import Dataset\n",
        "\n",
        "df = pd.read_csv(\"e2e-dataset/trainset.csv\")\n",
        "custom_dataset = Dataset.from_pandas(df)"
      ],
      "id": "MLqz11UioD4Q"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lRauc5JBoD4R"
      },
      "outputs": [],
      "source": [
        "import csv\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "\n",
        "class E2EDataset(Dataset):\n",
        "    \"\"\"Tokenize data when we call __getitem__\"\"\"\n",
        "    def __init__(self, path, tokenizer):\n",
        "        with open(path, newline=\"\") as f:\n",
        "            reader = csv.reader(f)\n",
        "            next(reader) # skip the heading\n",
        "            self.data = [{\"source\": row[0], \"target\": row[1]} for row in reader]\n",
        "        self.tokenizer = tokenizer\n",
        "\n",
        "    def __getitem__(self, i):\n",
        "        inputs = self.tokenizer(self.data[i]['source'])\n",
        "        labels = self.tokenizer(self.data[i]['target'])\n",
        "        inputs['labels'] = labels.input_ids\n",
        "        return inputs\n"
      ],
      "id": "lRauc5JBoD4R"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "eRu5mFIpoD4R"
      },
      "outputs": [],
      "source": [
        "bart_tokenizer = AutoTokenizer.from_pretrained('facebook/bart-base')"
      ],
      "id": "eRu5mFIpoD4R"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I32zL1nEoD4S"
      },
      "outputs": [],
      "source": [
        "dataset = E2EDataset(\"e2e-dataset/trainset.csv\", bart_tokenizer)"
      ],
      "id": "I32zL1nEoD4S"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I50Sh862oD4T"
      },
      "outputs": [],
      "source": [
        "bart_tokenizer.prepare_seq2seq_batch(src_texts=[\"This is the first test.\", \"This is the second test.\"], tgt_texts=[\"Target 1\", \"Target 2\"], return_tensors=\"pt\")"
      ],
      "id": "I50Sh862oD4T"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w8frTRD3oD4T"
      },
      "outputs": [],
      "source": [
        "dataset[0]"
      ],
      "id": "w8frTRD3oD4T"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tHI3KuNZ-X8w"
      },
      "source": [
        "## Appendix 2: Pipelines\n",
        "\n",
        "There are some standard NLP tasks like sentiment classification or question answering where there are already pre-trained (and fine-tuned!) models available through Hugging Face Transformer's [_Pipeline_](https://huggingface.co/docs/transformers/v4.16.2/en/main_classes/pipelines#transformers.pipeline) interface.\n",
        "\n",
        "For your projects, you likely won't be using it too much, but it's still worth knowing about!\n",
        "\n",
        "Here's an example with Sentiment Analysis:"
      ],
      "id": "tHI3KuNZ-X8w"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "gOj5ODS0-X8x"
      },
      "outputs": [],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "sentiment_analysis = pipeline(\"sentiment-analysis\", model=\"siebert/sentiment-roberta-large-english\")"
      ],
      "id": "gOj5ODS0-X8x"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D5wZuMG2-X8y"
      },
      "source": [
        "You can run the pipeline by just calling it on a string"
      ],
      "id": "D5wZuMG2-X8y"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GrygLkiQ-X8y"
      },
      "outputs": [],
      "source": [
        "sentiment_analysis(\"Hugging Face Transformers is really cool!\")"
      ],
      "id": "GrygLkiQ-X8y"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0e2E8qKH-X8z"
      },
      "source": [
        "Or on a list of strings:"
      ],
      "id": "0e2E8qKH-X8z"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EpBvCpVM-X8z"
      },
      "outputs": [],
      "source": [
        "sentiment_analysis([\"I didn't know if I would like Hákarl, but it turned out pretty good.\",\n",
        "                    \"I didn't know if I would like Hákarl, and it was just as bad as I'd heard.\"])"
      ],
      "id": "EpBvCpVM-X8z"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ptc0BViy-X80"
      },
      "source": [
        "You can find more information on pipelines (including which ones are available) [here](https://huggingface.co/docs/transformers/main_classes/pipelines)"
      ],
      "id": "Ptc0BViy-X80"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3oRAmG_w-X9H"
      },
      "source": [
        "## Appendix 4: Masked Language Modeling"
      ],
      "id": "3oRAmG_w-X9H"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "ZXD2-Gsu-X9H"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoModelForMaskedLM\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\", fast=True)\n",
        "bert = AutoModelForMaskedLM.from_pretrained(\"bert-base-cased\")"
      ],
      "id": "ZXD2-Gsu-X9H"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fvfHGKrq-X9I"
      },
      "outputs": [],
      "source": [
        "prompt = \"I am [MASK] to learn about HuggingFace!\"\n",
        "model = pipeline(\"fill-mask\", \"bert-base-cased\")\n",
        "model(prompt)"
      ],
      "id": "fvfHGKrq-X9I"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0B2qek-v-X9I",
        "scrolled": true
      },
      "outputs": [],
      "source": [
        "inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
        "mask_index = np.where(inputs['input_ids'] == tokenizer.mask_token_id)\n",
        "outputs = bert(**inputs)\n",
        "top_5_predictions = torch.softmax(outputs.logits[mask_index], dim=1).topk(5)\n",
        "\n",
        "print(prompt)\n",
        "for i in range(5):\n",
        "    prediction = tokenizer.decode(top_5_predictions.indices[0, i])\n",
        "    prob = top_5_predictions.values[0, i]\n",
        "    print(f\"  {i+1}) {prediction}\\t{prob:.3f}\")"
      ],
      "id": "0B2qek-v-X9I"
    }
  ],
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