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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/educatorsRlearners/hugging_face_course/blob/main/03_fine_tuning_a_pretrained_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R8M4jBBPGnQu"
      },
      "source": [
        "# Chapter 3. Fine-Tuning a pretrained model\n",
        "\n",
        "It's all well and good to use toy data sets, but what about fine-tuning a pretrained model for your own? \n",
        "\n",
        "In this chapter we'll: \n",
        "\n",
        "\n",
        "*   prepare a large dataset from the Hub\n",
        "*   use the ```Trainer``` API to fine-tune a model\n",
        "*   use a custom training loop\n",
        "*   leverage the  🤗 Accelerate library to run a custom training loop on a distributed setup\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers datasets accelerate"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3011583a-a806-4fea-e3bb-7298366a7b9b"
      },
      "execution_count": 4,
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        {
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          "name": "stdout",
          "text": [
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            "Collecting accelerate\n",
            "  Downloading accelerate-0.5.1-py3-none-any.whl (58 kB)\n",
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            "Installing collected packages: accelerate\n",
            "Successfully installed accelerate-0.5.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## [Processing the data](https://huggingface.co/course/chapter3/2?fw=pt)\n",
        "\n",
        "This is how we would train a sequence classifier on one batch using PyTorch: "
      ],
      "metadata": {
        "id": "gGJWXsSTYQs2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch \n",
        "from transformers import AdamW, AutoTokenizer, AutoModelForSequenceClassification\n",
        "\n",
        "checkpoint = \"bert-base-uncased\"\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
        "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
        "\n",
        "sequences = [\n",
        "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
        "    \"This course is amazing!\",\n",
        "]\n",
        "\n",
        "batch = tokenizer(sequences, \n",
        "                  padding=True,\n",
        "                  truncation=True,\n",
        "                  return_tensors=\"pt\")\n",
        "\n",
        "batch[\"labels\"] = torch.tensor([1,1])\n",
        "\n",
        "optimizer = AdamW(model.parameters())\n",
        "\n",
        "loss = model(**batch).loss\n",
        "loss.backward()\n",
        "optimizer.step()"
      ],
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          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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              "version_minor": 0,
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight']\n",
            "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', '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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Of course the example above is just that: an example. If we want to get decent results, we're going to need to train on a much larger dataset. Now where can we find one of those? \n",
        "\n",
        "### Loading a dataset from the Hub\n"
      ],
      "metadata": {
        "id": "WMxMFdwpb1YJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
        "raw_datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652,
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        },
        "id": "suUYZIsKe-wz",
        "outputId": "6a99f254-805d-47b1-a3fe-5af5af72a4e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cb99730591b94ce991c867f8e390b1d7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/7.78k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5235913b2bb34f4499b799924c0e9ca7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/4.47k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset glue/mrpc (download: 1.43 MiB, generated: 1.43 MiB, post-processed: Unknown size, total: 2.85 MiB) to /root/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad...\n"
          ]
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        {
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              "model_id": "1de57e8a1f3046c6ae590efce6f66e75",
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              "version_major": 2
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        },
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              "model_id": "817fb1f589b24691be79a376f7d0c4af",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading: 0.00B [00:00, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ba1df4d3dd134296a41b85ca4f2b05c8",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading: 0.00B [00:00, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "134234e0e2c84ce3857e46755073875e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading: 0.00B [00:00, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "393a3c6dfa53485e8c289a2f77c35b5c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "05fe92894392486d8e0da6d199fb7be2",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "57ae2a63daec4b62b75a1805df56661e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset glue downloaded and prepared to /root/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bdfb66d3fdc245cb864bf1bad832d301",
              "version_minor": 0,
              "version_major": 2
            },
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
              "        num_rows: 3668\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
              "        num_rows: 408\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
              "        num_rows: 1725\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "If we want to inspect our data, we index it like a dictionary: "
      ],
      "metadata": {
        "id": "y6ijydRMgN0p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "raw_train_dataset = raw_datasets[\"train\"]\n",
        "\n",
        "raw_train_dataset[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "661cxvLIexOJ",
        "outputId": "cab13d1f-63bc-47ce-83f5-680a9d1653f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'idx': 0,\n",
              " 'label': 1,\n",
              " 'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
              " 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What are the different labels for the dataset above? "
      ],
      "metadata": {
        "id": "u4T6J_zNgd58"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "raw_train_dataset.features[\"label\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9aaAHYdvgX6T",
        "outputId": "9539d7c6-daa0-48f8-d88a-8454eeffd831"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that we know what's in our dataset, we can start...\n",
        "\n",
        "### Preprocessing a dataset\n",
        "\n",
        "We've previously preprocessed single sentences but what about pairs of sentences? For instance, what if we want to train a model for if a sentence natrually follows on from the previous one, if questions are duplicates, or if there is plagiarism? \n",
        "\n",
        "To do so, we need to preporcess sentence pairs like this: "
      ],
      "metadata": {
        "id": "aXYIbXdOg5QQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_pairs = tokenizer(\"My name is Evan.\", \"I work for the AI Guild\")\n",
        "\n",
        "print(tokenized_pairs['input_ids'])\n",
        "print(tokenized_pairs['token_type_ids'])\n",
        "print(tokenized_pairs[\"attention_mask\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jK-zc-rrk9PW",
        "outputId": "15599235-9c60-46d6-c11a-584f1f9a68a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[101, 2026, 2171, 2003, 9340, 1012, 102, 1045, 2147, 2005, 1996, 9932, 9054, 102]\n",
            "[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]\n",
            "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Key Point**: ```token_type_ids```, (i.e., which tokens belong to which sequence) is only returned by models which are trained to handle multiple sentences. \n",
        "\n",
        "For example, BERT is trained on masked language tasks as well as sequencing (i.e., \"Does this sentnece naturally follow on from the previous one?\"). Distil-BERT, on the other hand, does not.\n",
        "\n",
        "BTW, if we have several pairs, we can pass them like this: "
      ],
      "metadata": {
        "id": "mLVZVxhGmOqN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_groups = tokenizer(\n",
        "    [\"My name is Evan.\", \"I'm going to the movies.\"], #first sentences\n",
        "    [\"I work for the AI Guild.\", \"This song rocks!\"], \n",
        "    padding=True\n",
        ")\n",
        "\n",
        "print(tokenized_groups['input_ids'])\n",
        "print(tokenized_groups['token_type_ids'])\n",
        "print(tokenized_groups[\"attention_mask\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fYvE89UTlcKn",
        "outputId": "9780bc53-f2df-4f43-8e03-1894b231a74b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[101, 2026, 2171, 2003, 9340, 1012, 102, 1045, 2147, 2005, 1996, 9932, 9054, 1012, 102], [101, 1045, 1005, 1049, 2183, 2000, 1996, 5691, 1012, 102, 2023, 2299, 5749, 999, 102]]\n",
            "[[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now while the above method works, it causes some issues in that it returns a dictionary so we'll probably end up running out of RAM unless we're using a toy dataset. \n",
        "\n",
        "To avoid this issue, we can use the ```Dataset.map()``` method so as to keep our data as an Apache Arrow file. "
      ],
      "metadata": {
        "id": "A_FkXuVHoYaj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_function(example):\n",
        "  return tokenizer(example[\"sentence1\"], example[\"sentence2\"],\n",
        "                   truncation=True)"
      ],
      "metadata": {
        "id": "4jyeixjenrS0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Key Point**: we've omitted ```padding=True``` because padding is best done during batching because we don't need the length of the samples to match until they are ready to go into training; otherwise, we're just creating a massive dataset. \n",
        "\n",
        "Now we can apply our tokenize function on the dataset in one go like so: "
      ],
      "metadata": {
        "id": "IAU_A9JPpZJA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
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        "outputId": "def4659a-3da7-42a3-cab9-c2b5d4b6d37b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a8d90fa5cd074ea0a0575f0fb1341c77",
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              "  0%|          | 0/4 [00:00<?, ?ba/s]"
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        {
          "output_type": "display_data",
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              "model_id": "2c7afab0f25f4e3f831feb10d5f41dba",
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              "  0%|          | 0/1 [00:00<?, ?ba/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "638d4365c62f450c95156745a77cbb6f",
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              "  0%|          | 0/2 [00:00<?, ?ba/s]"
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7gApyL0sqQhK",
        "outputId": "22264d0b-69fe-4584-db1a-b3eaaca7ecb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
              "        num_rows: 3668\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
              "        num_rows: 408\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
              "        num_rows: 1725\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dynamic Padding\n",
        "\n",
        "Fixed padding is padding every input to a given length. \n",
        "\n",
        "Dynamic padding is padding every input to the max lenght of the longest input for a given batch. \n",
        "\n",
        "**NB**: DO NOT use dynamic padding on a TPU.\n",
        "\n",
        "How do we do dynamic padding with the ```transformers``` library? \n",
        "\n",
        "Like so:"
      ],
      "metadata": {
        "id": "IldsuuWFqxp5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DataCollatorWithPadding\n",
        "\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
      ],
      "metadata": {
        "id": "5dXiqRqrqdXA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "It really is just that simple. \n",
        "\n",
        "Now let's try it out with some samples from our training data."
      ],
      "metadata": {
        "id": "WIhN85g8sdcP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "samples = tokenized_datasets['train'][:8]\n",
        "samples = {k: v for k, v in samples.items() if k not in [\"idx\", \"sentence1\", \"sentence2\"]}\n",
        "[len(x) for x in samples[\"input_ids\"]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ctjqSmCeqoN0",
        "outputId": "80a792a3-1206-41fe-cfbb-12a371dffdd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[50, 59, 47, 67, 59, 50, 62, 32]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So, based on what we see above, we need to pad this batch to a max length of 67. "
      ],
      "metadata": {
        "id": "dARzjG69urDk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "batch = data_collator(samples)\n",
        "\n",
        "{k:v.shape for k, v in batch.items()}"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Irbg6O09sqmj",
        "outputId": "0d99b954-b655-4a32-d3b1-0cefc3558836"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'attention_mask': torch.Size([8, 67]),\n",
              " 'input_ids': torch.Size([8, 67]),\n",
              " 'labels': torch.Size([8]),\n",
              " 'token_type_ids': torch.Size([8, 67])}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## [Fine-tuning a model with the Trainer API](https://huggingface.co/course/chapter3/3?fw=pt)\n",
        "\n",
        "The ```Trainer``` api allows us to easily train our model on our custom dataset by passing a myriad of specifications such as: \n",
        "\n",
        "* metrics\n",
        "* hyperparametes\n",
        "* model\n",
        "* training, validation, and test datasets\n",
        "* tokenizer\n",
        "* data collator\n",
        "\n",
        "The trainer will then output the: \n",
        "* training\n",
        "* evaluation\n",
        "* prediction\n",
        "\n",
        "**Key Point**: ```Trainer``` runs incredibly slowly on CPU be sure to use either Colab or Kaggle. \n",
        "\n",
        "Now that that's covered, the first step is to preprocess the data."
      ],
      "metadata": {
        "id": "_sPEmwN3vHh5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "from transformers import  AutoTokenizer, DataCollatorWithPadding\n",
        "\n",
        "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
        "checkpoint = 'bert-base-uncased'\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
        "\n",
        "def tokenize_function(example):\n",
        "  return tokenizer(example['sentence1'], example['sentence2'], truncation=True)\n",
        "\n",
        "\n",
        "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
        "\n"
      ],
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        "outputId": "95d79c10-f768-40f6-b31b-dd001486fdab"
      },
      "execution_count": null,
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        {
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset glue/mrpc (download: 1.43 MiB, generated: 1.43 MiB, post-processed: Unknown size, total: 2.85 MiB) to /root/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad...\n"
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          "metadata": {}
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              "0 examples [00:00, ? examples/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset glue downloaded and prepared to /root/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad. Subsequent calls will reuse this data.\n"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "### Training \n",
        "\n",
        "Before we define the ```Trainer``` we'll want to define the ```TrainingArguments``` like so: "
      ],
      "metadata": {
        "id": "1Urk0MGH2Fl3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "training_args = TrainingArguments('test-trainer')"
      ],
      "metadata": {
        "id": "FG6080RO1_MQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Key Point**: To automatically push the model to the hub during training, pass ```push_to_hub=True``` in ```TrainingArguments```.\n",
        "\n",
        "Next, we define our model: "
      ],
      "metadata": {
        "id": "PQRqOoc82jwN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    pretrained_model_name_or_path=checkpoint,\n",
        "    num_labels=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161,
          "referenced_widgets": [
            "2a61569c943448a4a19e58a832e037e9",
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            "8f22d3c4fd4245bc90c7a09cb1842845",
            "9063b5cac8f941d99488f2f44ac2c37d"
          ]
        },
        "id": "Gatc2Pkj2etV",
        "outputId": "dcbdc008-ef5f-41e5-ad33-de3b0f878a58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2a61569c943448a4a19e58a832e037e9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/420M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight']\n",
            "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we can define our ```Trainer``` by passing all the objects we've created to this point:"
      ],
      "metadata": {
        "id": "yTefasH43lLx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import  Trainer\n",
        "\n",
        "trainer = Trainer(\n",
        "    model,\n",
        "    training_args,\n",
        "    train_dataset=tokenized_datasets[\"train\"],\n",
        "    eval_dataset=tokenized_datasets['validation'],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer\n",
        ")"
      ],
      "metadata": {
        "id": "yDKPnzVw3LRi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**NB**: When you pass the ```toknizer``` as we did above, you do not need to define the ```data_collator``` as we did above. However, in the spirit of explicit is better than implicit, we did it anyways 😀\n",
        "\n",
        "Now, to fine-tune the model on our custom dataset, we simply call the ```train()``` method on our ```Trainer```:"
      ],
      "metadata": {
        "id": "Vd1ppAb44Xpk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "6lPXgFZe4GaG",
        "outputId": "f9aa7f9d-c0dc-4782-87a0-4121b9bf6ef2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the training set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: sentence2, sentence1, idx.\n",
            "***** Running training *****\n",
            "  Num examples = 3668\n",
            "  Num Epochs = 3\n",
            "  Instantaneous batch size per device = 8\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 1377\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1377' max='1377' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1377/1377 03:26, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.570800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.368700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to test-trainer/checkpoint-500\n",
            "Configuration saved in test-trainer/checkpoint-500/config.json\n",
            "Model weights saved in test-trainer/checkpoint-500/pytorch_model.bin\n",
            "tokenizer config file saved in test-trainer/checkpoint-500/tokenizer_config.json\n",
            "Special tokens file saved in test-trainer/checkpoint-500/special_tokens_map.json\n",
            "Saving model checkpoint to test-trainer/checkpoint-1000\n",
            "Configuration saved in test-trainer/checkpoint-1000/config.json\n",
            "Model weights saved in test-trainer/checkpoint-1000/pytorch_model.bin\n",
            "tokenizer config file saved in test-trainer/checkpoint-1000/tokenizer_config.json\n",
            "Special tokens file saved in test-trainer/checkpoint-1000/special_tokens_map.json\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1377, training_loss=0.40630322846278993, metrics={'train_runtime': 206.6487, 'train_samples_per_second': 53.25, 'train_steps_per_second': 6.663, 'total_flos': 405470580750720.0, 'train_loss': 0.40630322846278993, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We didn't get any idea on how our model did during training since we didn't provide the ```Trainer``` with a metric to compute the loss. Let's remedy that 😀\n",
        "\n",
        "### [Evaluation](https://huggingface.co/course/chapter3/3?fw=pt#evaluation)\n",
        "\n",
        "To get some predictions from our model, we use the ```predict()``` method for the ```Trainer```:\n"
      ],
      "metadata": {
        "id": "akeWVphS5bpL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = trainer.predict(tokenized_datasets[\"validation\"])\n",
        "print(predictions.predictions.shape, predictions.label_ids.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148
        },
        "id": "NkrCpWQa5Big",
        "outputId": "a43cbf6d-5832-4d88-fdfb-ae9090779bd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the test set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: sentence2, sentence1, idx.\n",
            "***** Running Prediction *****\n",
            "  Num examples = 408\n",
            "  Batch size = 8\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='102' max='51' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [51/51 00:19]\n",
              "    </div>\n",
              "    "
            ],
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              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(408, 2) (408,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "```predictions``` is a 2D array comprising the logits for our predictions"
      ],
      "metadata": {
        "id": "C_xlymnr60W9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions.predictions[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5yi1ylfq6hTB",
        "outputId": "f5aa8d19-b6b3-4705-d843-aba0ce8941e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-3.0714695,  2.9938512], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In order to actually compare our predictions to our labels, we need to take the index with the max value on the second axis: "
      ],
      "metadata": {
        "id": "Lo1fLUPP72dU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np \n",
        "\n",
        "preds = np.argmax(predictions.predictions, axis=-1)"
      ],
      "metadata": {
        "id": "fh83wqIC6yE0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we can compare our predictions with the labels: "
      ],
      "metadata": {
        "id": "f6GjKpLS7wc5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_metric\n",
        "\n",
        "metric = load_metric(\"glue\", \"mrpc\")\n",
        "metric.compute(predictions=preds, references=predictions.label_ids)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "aa6d111ab9a44ab8bc824bc8f12bed51",
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        "outputId": "0241c933-96a5-4701-e835-17786d0ccfda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "aa6d111ab9a44ab8bc824bc8f12bed51",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.86k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 0.8529411764705882, 'f1': 0.8993288590604027}"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that we've done everything step-by-step, we can wrap everything up in a function and pass it to the ```Trainer``` both ```training_args``` and ```compute_metrics```:"
      ],
      "metadata": {
        "id": "XUa65BZg8hRO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics(eval_preds):\n",
        "  metric = load_metric(\"glue\", \"mrpc\")\n",
        "  logits, labels = eval_preds\n",
        "  predictions = np.argmax(logits, axis=-1)\n",
        "  return metric.compute(predictions=predictions, references=labels)"
      ],
      "metadata": {
        "id": "IkZAWTyA8Xrh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_args = TrainingArguments('test-trainer', evaluation_strategy='epoch')\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    pretrained_model_name_or_path=checkpoint,\n",
        "    num_labels=2\n",
        ")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model,\n",
        "    training_args,\n",
        "    train_dataset=tokenized_datasets['train'],\n",
        "    eval_dataset=tokenized_datasets['validation'],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CWnjubnt9QXo",
        "outputId": "52051202-7ea5-4d23-f1be-fcaa98ba5034"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "PyTorch: setting up devices\n",
            "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
            "loading configuration file https://huggingface.co/bert-base-uncased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/3c61d016573b14f7f008c02c4e51a366c67ab274726fe2910691e2a761acf43e.37395cee442ab11005bcd270f3c34464dc1704b715b5d7d52b1a461abe3b9e4e\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"bert-base-uncased\",\n",
            "  \"architectures\": [\n",
            "    \"BertForMaskedLM\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.15.0\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 30522\n",
            "}\n",
            "\n",
            "loading weights file https://huggingface.co/bert-base-uncased/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/a8041bf617d7f94ea26d15e218abd04afc2004805632abc0ed2066aa16d50d04.faf6ea826ae9c5867d12b22257f9877e6b8367890837bd60f7c54a29633f7f2f\n",
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight']\n",
            "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "All systems are now go! "
      ],
      "metadata": {
        "id": "hKBmVVM3-L71"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 853
        },
        "id": "_Hj5QpMw9gTs",
        "outputId": "10beb54a-49bb-45be-804a-a29b145f1647"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the training set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: sentence2, sentence1, idx.\n",
            "***** Running training *****\n",
            "  Num examples = 3668\n",
            "  Num Epochs = 3\n",
            "  Instantaneous batch size per device = 8\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 1377\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1377' max='1377' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1377/1377 03:35, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.387581</td>\n",
              "      <td>0.840686</td>\n",
              "      <td>0.886165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.535300</td>\n",
              "      <td>0.450535</td>\n",
              "      <td>0.850490</td>\n",
              "      <td>0.895726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.283600</td>\n",
              "      <td>0.650574</td>\n",
              "      <td>0.850490</td>\n",
              "      <td>0.896435</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the evaluation set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: sentence2, sentence1, idx.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 408\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to test-trainer/checkpoint-500\n",
            "Configuration saved in test-trainer/checkpoint-500/config.json\n",
            "Model weights saved in test-trainer/checkpoint-500/pytorch_model.bin\n",
            "tokenizer config file saved in test-trainer/checkpoint-500/tokenizer_config.json\n",
            "Special tokens file saved in test-trainer/checkpoint-500/special_tokens_map.json\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: sentence2, sentence1, idx.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 408\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to test-trainer/checkpoint-1000\n",
            "Configuration saved in test-trainer/checkpoint-1000/config.json\n",
            "Model weights saved in test-trainer/checkpoint-1000/pytorch_model.bin\n",
            "tokenizer config file saved in test-trainer/checkpoint-1000/tokenizer_config.json\n",
            "Special tokens file saved in test-trainer/checkpoint-1000/special_tokens_map.json\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: sentence2, sentence1, idx.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 408\n",
            "  Batch size = 8\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1377, training_loss=0.3330001664836942, metrics={'train_runtime': 215.8797, 'train_samples_per_second': 50.973, 'train_steps_per_second': 6.379, 'total_flos': 405470580750720.0, 'train_loss': 0.3330001664836942, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## [A full training](https://huggingface.co/course/chapter3/4?fw=pt) \n",
        "\n",
        "Now we're going to do the same thing we did before but using PyTorch instead of ```Trainer```. \n",
        "\n",
        "*Why?* you may ask?\n",
        "\n",
        "Simply put, sometimes it's easier to build it yourself rather than customize an existing class. \n",
        "\n",
        "We'll start by preprocessing our data the same way we've don in the past: "
      ],
      "metadata": {
        "id": "4wtGuQ8mBGJC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "from transformers import  AutoTokenizer, DataCollatorWithPadding\n",
        "\n",
        "raw_datasets = load_dataset(\"glue\", 'mrpc')\n",
        "checkpoint = \"bert-base-uncased\"\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
        "\n",
        "def tokenize_function(example):\n",
        "  return tokenizer(example['sentence1'], example['sentence2'],\n",
        "                   truncation=True)\n",
        "\n",
        "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer) "
      ],
      "metadata": {
        "id": "MQ2cr3Vg-N3Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
            "6d1ffebec1ec418e95012b78d66649e4",
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            "2e139aaac0754200ae1a8349d7c62614",
            "1da07355fae14017bd60b14a6b69a1ff"
          ]
        },
        "outputId": "f83f0d03-a14a-4201-bf18-11ab1d893c68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Reusing dataset glue (/root/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6d1ffebec1ec418e95012b78d66649e4",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-abc77336bc77d9f8.arrow\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f528fba1f457494a8d25e6da049dea6e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-1dbb17d82fd35b0a.arrow\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Prepare for training\n",
        "\n",
        "Let's inspect our tokenized datasets to see what we have:"
      ],
      "metadata": {
        "id": "VHgLLvcO0wYA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "21uP8ozhy_m7",
        "outputId": "1b28a61a-9e5e-4bda-a632-798ab251e078"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
              "        num_rows: 3668\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
              "        num_rows: 408\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
              "        num_rows: 1725\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ok, we can see that we have some work to do:\n",
        "\n",
        "1.   Remove columns we won't feed to our model\n",
        "2.   Rename ```label``` to ```labels``` because that's what the model expects\n",
        "3.   Convert the datasets to PyTorch tensors instead of lists\n",
        "\n"
      ],
      "metadata": {
        "id": "E1jblJZv1BbG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets = tokenized_datasets.remove_columns([\"sentence1\", \"sentence2\", \"idx\"])\n",
        "tokenized_datasets = tokenized_datasets.rename_column(\"label\", 'labels')\n",
        "tokenized_datasets.set_format(\"torch\")"
      ],
      "metadata": {
        "id": "xrIsGece0q3n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check to see if it worked\n",
        "tokenized_datasets['train'].column_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UnNVb3XR17Xb",
        "outputId": "f0058ec8-8c06-499a-b459-1c3d0b3c7b58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['attention_mask', 'input_ids', 'labels', 'token_type_ids']"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we create our dataloaders: "
      ],
      "metadata": {
        "id": "nKxuTQ_E2so1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "data_loader_args = {\"shuffle\":True,\n",
        "    \"batch_size\":8,\n",
        "    \"collate_fn\":data_collator}\n",
        "\n",
        "train_dataloader = DataLoader(\n",
        "    tokenized_datasets[\"train\"],\n",
        "    **data_loader_args)\n",
        "    \n",
        "eval_dataloader = DataLoader(\n",
        "    tokenized_datasets[\"validation\"],\n",
        "    **data_loader_args\n",
        ")"
      ],
      "metadata": {
        "id": "Z87vSkxG2Ot5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's do a quick sanity check:"
      ],
      "metadata": {
        "id": "sshMPQSF3zE3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for batch in train_dataloader:\n",
        "  break\n",
        "{k: v.shape for k, v in batch.items()}"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AFgC9cqF3k0x",
        "outputId": "bd9251e4-535a-4463-cc8b-ddb9a6d5d998"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'attention_mask': torch.Size([8, 76]),\n",
              " 'input_ids': torch.Size([8, 76]),\n",
              " 'labels': torch.Size([8]),\n",
              " 'token_type_ids': torch.Size([8, 76])}"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Key Point**: We are using dynamic padding meaning we're padding to the maximum length of each batch. As such, every time we run the code above, we'll get a differnt shape.\n",
        "\n",
        "Given that all our preprocessing is FINALLY finished, we can move on to creating our model."
      ],
      "metadata": {
        "id": "FbFTp6aS4TFy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    checkpoint,\n",
        "    num_labels=2\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161,
          "referenced_widgets": [
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            "dc3f264909de4d7d9d683be05191ba3b",
            "9f69698eae8d4d3e9f62e6192ea1c0e7"
          ]
        },
        "id": "2w1hy1LW36Fj",
        "outputId": "4b407b72-f375-4c49-c231-3ccf8e7492a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1b8fd812bd704cf7a6a67bd66f773f86",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/420M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', '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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As always, we'll do a quick sanity check: "
      ],
      "metadata": {
        "id": "JkIopSQl5M1R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "outputs = model(**batch)\n",
        "\n",
        "print(outputs.loss, outputs.logits.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wsofNmoZ4_it",
        "outputId": "24e87c83-bb86-4a61-da1c-c0ce815c7b5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(0.6909, grad_fn=<NllLossBackward0>) torch.Size([8, 2])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Excellent! We passed a batch of eight samples and got our result back with two logits (one for each label) hence the shape is [8, 2]. \n",
        "\n",
        "Next up, instantiate a learning rate scheduler along with an optimizer and, since ```Trainer``` uses AdamW by default, we'll use the same one here."
      ],
      "metadata": {
        "id": "7M7uacmU5iwy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AdamW\n",
        "\n",
        "optimizer = AdamW(model.parameters(), \n",
        "                  lr=5e-5)"
      ],
      "metadata": {
        "id": "w-irAoB75YUs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's schedule the learning rate to increase from 5e-5 to 0. "
      ],
      "metadata": {
        "id": "Smza9mM97CYm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import get_scheduler\n",
        "\n",
        "num_epochs = 3\n",
        "\n",
        "num_training_steps = num_epochs * len(train_dataloader)\n",
        "\n",
        "lr_scheduler = get_scheduler(\n",
        "    \"linear\",\n",
        "    optimizer=optimizer,\n",
        "    num_warmup_steps=0,\n",
        "    num_training_steps=num_training_steps\n",
        ")"
      ],
      "metadata": {
        "id": "rp9nAxa45y43"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### The training loop\n",
        "\n",
        "Finally, we can train! All we have to do now is: \n",
        "\n",
        "*   define our device\n",
        "*   add our model to the device\n",
        "*   write our loop\n",
        "\n"
      ],
      "metadata": {
        "id": "JhZqG5LG7qW0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device('cpu')\n",
        "\n",
        "model.to(device)\n",
        "\n",
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gj5lgpo87ozh",
        "outputId": "3cc3699c-edb6-4d88-8d7f-0e5954305650"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm.auto import tqdm\n",
        "\n",
        "progress_bar = tqdm(range(num_training_steps))\n",
        "\n",
        "model.train()\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "  for batch in train_dataloader:\n",
        "    batch = {k: v.to(device) for k, v in batch.items()}\n",
        "    outputs = model(**batch)\n",
        "    loss = outputs.loss\n",
        "    loss.backward()\n",
        "\n",
        "    optimizer.step()\n",
        "    lr_scheduler.step()\n",
        "    optimizer.zero_grad()\n",
        "    progress_bar.update(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 48,
          "referenced_widgets": [
            "439d60de239a4b7e96e8d805572ff2a3",
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            "51920567533c420e84f2f5d758643f81",
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            "6ec1bb622ef0407fbdb31988d3eb31de",
            "1cfd2d346ebc4ddcb51b4556f88f08eb",
            "a18d5f93dd094df794b858ce6ca6ef16",
            "01a894f8619141a283613c97407e965d",
            "1d234009166e45d8a0b8745ebe71fcc4"
          ]
        },
        "id": "1F_Qs5M08Rq7",
        "outputId": "1db4551d-642a-4cb4-8156-6f620f82bbd4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "439d60de239a4b7e96e8d805572ff2a3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1377 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### The evaluation loop\n",
        "\n",
        "We just need to load the metrics and compute the loss:"
      ],
      "metadata": {
        "id": "Cld-Mc139vXf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_metric\n",
        "\n",
        "metric = load_metric(\"glue\", \"mrpc\")\n",
        "model.eval()\n",
        "for batch in eval_dataloader:\n",
        "  batch = {k: v.to(device) for k, v in batch.items()}\n",
        "  with torch.no_grad():\n",
        "    outputs = model(**batch)\n",
        "  \n",
        "  logits = outputs.logits\n",
        "  predictions = torch.argmax(logits, dim=-1)\n",
        "  metric.add_batch(predictions=predictions,\n",
        "                   references=batch[\"labels\"])\n",
        "\n",
        "metric.compute()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "977c1c0abacd4cdf9500446c5457ad37",
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            "34899a4dc40648bd82b92e75a3e8cbf8",
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            "5bae5940af1c47418bad0a26044c04e2",
            "557e02aff9094c9882cc7fb99e2b4d68"
          ]
        },
        "id": "9z55QYNb83ds",
        "outputId": "dbbdab4e-e41b-4718-ff15-47cfc5c1fbc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "977c1c0abacd4cdf9500446c5457ad37",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.86k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 0.8553921568627451, 'f1': 0.8998302207130731}"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### [Supercharge your training loop with 🤗Accelerate](https://huggingface.co/course/chapter3/4?fw=pt#supercharge-your-training-loop-with-%F0%9F%A4%97-accelerate)\n",
        "\n",
        "If we have multipe GPUs or TPUs at our disposal, we can use the 🤗Accelerate API with minimal tweaks. \n",
        "\n",
        "One of the key tweeks is to set padding to ```max_length```.\n",
        "\n",
        "Next point, save the following code in a script called ```train.py```."
      ],
      "metadata": {
        "id": "D3TRt8-Q-7r-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from accelerate import Accelerator\n",
        "from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n",
        "\n",
        "accelerator = Accelerator()\n",
        "\n",
        "checkpoint = \"bert-base-uncased\"\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
        "optimizer = AdamW(model.parameters(), lr=3e-5)\n",
        "\n",
        "train_dl, eval_dl, model, optimizer = accelerator.prepare(\n",
        "    train_dataloader, eval_dataloader, model, optimizer\n",
        ")\n",
        "\n",
        "num_epochs = 3\n",
        "num_training_steps = num_epochs * len(train_dl)\n",
        "lr_scheduler = get_scheduler(\n",
        "    \"linear\",\n",
        "    optimizer=optimizer,\n",
        "    num_warmup_steps=0,\n",
        "    num_training_steps=num_training_steps,\n",
        ")\n",
        "\n",
        "progress_bar = tqdm(range(num_training_steps))\n",
        "\n",
        "model.train()\n",
        "for epoch in range(num_epochs):\n",
        "    for batch in train_dl:\n",
        "        outputs = model(**batch)\n",
        "        loss = outputs.loss\n",
        "        accelerator.backward(loss)\n",
        "\n",
        "        optimizer.step()\n",
        "        lr_scheduler.step()\n",
        "        optimizer.zero_grad()\n",
        "        progress_bar.update(1)"
      ],
      "metadata": {
        "id": "OqzTck5cLNWj"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}