01_Fine_Tuning_flan_t5_imdb_text_classification.ipynb

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      "metadata": {
        "id": "BYiw7RHYP9fl"
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      "source": [
        "# Fine-tune FLAN-T5 for IMDB text classification\n",
        "\n",
        "In this blog, you will learn how to fine-tune [google/flan-t5-xl](https://huggingface.co/google/flan-t5-base) for imdb text classification using Hugging Face Transformers. If you already know T5, FLAN-T5 is just better at everything. For the same number of parameters, these models have been fine-tuned on more than 1000 additional tasks covering also more languages. \n",
        "\n",
        "In this example we will use the [imdb](https://huggingface.co/datasets/imdb) Large Movie Review Dataset. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.\n",
        "\n",
        "You will learn how to:\n",
        "\n",
        "1. [Setup Development Environment](#1-setup-development-environment)\n",
        "2. [Load and prepare imdb dataset](#2-load-and-prepare-imdb-dataset)\n",
        "3. [Fine-tune and evaluate FLAN-T5](#3-fine-tune-and-evaluate-flan-t5)\n",
        "4. [Run Inference and run inference and classification report](#4-run-inference-run-inference-and-classification-report)\n",
        "\n",
        "Before we can start, make sure you have a [Hugging Face Account](https://huggingface.co/join) to save artifacts and experiments. \n",
        "\n",
        "## Quick intro: FLAN-T5, just a better T5\n",
        "\n",
        "FLAN-T5 released with the [Scaling Instruction-Finetuned Language Models](https://arxiv.org/pdf/2210.11416.pdf) paper is an enhanced version of T5 that has been finetuned in a mixture of tasks. The paper explores instruction finetuning with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on chain-of-thought data. The paper discovers that overall instruction finetuning is a general method for improving the performance and usability of pretrained language models. \n",
        "\n",
        "![flan-t5](https://github.com/philschmid/deep-learning-pytorch-huggingface/blob/main/assets/flan-t5.png?raw=1)\n",
        "\n",
        "* Paper: https://arxiv.org/abs/2210.11416\n",
        "* Official repo: https://github.com/google-research/t5x\n",
        "* github repo: https://github.com/M-Taghizadeh/flan-t5-base-imdb-text-classification\n",
        "--- \n",
        "\n",
        "Now we know what FLAN-T5 is, let's get started. 🚀\n",
        "\n",
        "_Note: This tutorial was created and run by [Mohammad Taghizaedh](https://github.com/M-Taghizadeh)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nPKzuFL8P9fs"
      },
      "source": [
        "## 1. Setup Development Environment\n",
        "\n",
        "Our first step is to install the Hugging Face Libraries, including transformers and datasets. Running the following cell will install all the required packages. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SKkT6q4yP9fu"
      },
      "outputs": [],
      "source": [
        "# !pip install pytesseract transformers datasets evaluate rouge-score nltk tensorboard py7zr --upgrade\n",
        "!pip install pytesseract transformers==4.28.1 datasets evaluate rouge-score nltk tensorboard py7zr "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YT0Q3pM-P9fx"
      },
      "source": [
        "This example will use the [Hugging Face Hub](https://huggingface.co/models) as a remote model versioning service. To be able to push our model to the Hub, you need to register on the [Hugging Face](https://huggingface.co/join). \n",
        "If you already have an account, you can skip this step. \n",
        "After you have an account, we will use the `notebook_login` util from the `huggingface_hub` package to log into our account and store our token (access key) on the disk. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
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        "id": "D4OIDtAEP9fy",
        "outputId": "dbafe22c-1ba9-4935-ca33-2b831aace08c"
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      "outputs": [
        {
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            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CEcR1RUTBMr5"
      },
      "source": [
        "## Import dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jPfcxpCc3_Bd"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import re\n",
        "import glob\n",
        "from datasets import load_dataset\n",
        "import datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gzO3GxtLP9fz"
      },
      "source": [
        "## 2. Load and prepare imdb dataset\n",
        "\n",
        "we will use the [imdb](https://huggingface.co/datasets/imdb) Large Movie Review Dataset. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5sA_rRwFP9f0"
      },
      "outputs": [],
      "source": [
        "dataset_id = \"imdb\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QWlQtnRnP9f0"
      },
      "source": [
        "To load the `samsum` dataset, we use the `load_dataset()` method from the 🤗 Datasets library.\n"
      ]
    },
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            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.31k [00:00<?, ?B/s]"
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            "text/plain": [
              "Downloading readme:   0%|          | 0.00/7.59k [00:00<?, ?B/s]"
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset imdb/plain_text to /root/.cache/huggingface/datasets/imdb/plain_text/1.0.0/d613c88cf8fa3bab83b4ded3713f1f74830d1100e171db75bbddb80b3345c9c0...\n"
          ]
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            "text/plain": [
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              "model_id": "5748fb675e404d0188f3c872912a9fe3",
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              "version_minor": 0
            },
            "text/plain": [
              "Generating train split:   0%|          | 0/25000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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              "Generating test split:   0%|          | 0/25000 [00:00<?, ? examples/s]"
            ]
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              "version_minor": 0
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            "text/plain": [
              "Generating unsupervised split:   0%|          | 0/50000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset imdb downloaded and prepared to /root/.cache/huggingface/datasets/imdb/plain_text/1.0.0/d613c88cf8fa3bab83b4ded3713f1f74830d1100e171db75bbddb80b3345c9c0. Subsequent calls will reuse this data.\n"
          ]
        },
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          "metadata": {},
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train dataset size: 25000\n",
            "Test dataset size: 25000\n"
          ]
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "# Load dataset from the hub\n",
        "dataset = load_dataset(dataset_id)\n",
        "\n",
        "print(f\"Train dataset size: {len(dataset['train'])}\")\n",
        "print(f\"Test dataset size: {len(dataset['test'])}\")\n",
        "\n",
        "# Train dataset size: 25000\n",
        "# Test dataset size: 25000"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SHEQjZk0Q0mn",
        "outputId": "55d80095-4414-4d8a-b770-1b7c2e458cff"
      },
      "outputs": [
        {
          "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",
              "})"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ricSiCLrP9f2"
      },
      "source": [
        "Lets checkout an example of the dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vgPKUEa1b_50",
        "outputId": "695a7a55-e261-45bc-9dea-4038ec2b618f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'text': '\"I Am Curious: Yellow\" is a risible and pretentious steaming pile. It doesn\\'t matter what one\\'s political views are because this film can hardly be taken seriously on any level. As for the claim that frontal male nudity is an automatic NC-17, that isn\\'t true. I\\'ve seen R-rated films with male nudity. Granted, they only offer some fleeting views, but where are the R-rated films with gaping vulvas and flapping labia? Nowhere, because they don\\'t exist. The same goes for those crappy cable shows: schlongs swinging in the breeze but not a clitoris in sight. And those pretentious indie movies like The Brown Bunny, in which we\\'re treated to the site of Vincent Gallo\\'s throbbing johnson, but not a trace of pink visible on Chloe Sevigny. Before crying (or implying) \"double-standard\" in matters of nudity, the mentally obtuse should take into account one unavoidably obvious anatomical difference between men and women: there are no genitals on display when actresses appears nude, and the same cannot be said for a man. In fact, you generally won\\'t see female genitals in an American film in anything short of porn or explicit erotica. This alleged double-standard is less a double standard than an admittedly depressing ability to come to terms culturally with the insides of women\\'s bodies.',\n",
              " 'label': 0}"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset['train'][1]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9NIR6GzgP9f3"
      },
      "source": [
        "To train our model we need to convert our inputs (text) to token IDs. This is done by a 🤗 Transformers Tokenizer. If you are not sure what this means check out [chapter 6](https://huggingface.co/course/chapter6/1?fw=tf) of the Hugging Face Course."
      ]
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            "c4bc8619391540139bc9c656e1ec2044",
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            "62c1081e477f4dadb7dd5a4f6a63a24a",
            "18fb0ac26cb74d78904efcb707dfd748",
            "237ab383bb894af0bf53daf9b60b88d9",
            "323f4ee3f3e248928521f3eda5c6065e",
            "8b49143517e442efa35c688fc373c582",
            "c823ff361ddb45b1beb4ae076d193652",
            "002c8d6a26d5408aaa75293c3ad4bf7f",
            "4d4595a35d8f427490d8cc15720b7667",
            "10f5be89e24647208da6c8c6fe95661e",
            "df21fff3049944b193418007288db9ee",
            "ab872874d7fc4142829e6425ed228e10",
            "136e11d1806541219608ab887994d6b0",
            "ef935e68c4be4ed5a4d25d69f0d811e9",
            "2cd0056afa6a44bbadd95b1b88368c8f",
            "52fcc17302c441daa6705752419e4006",
            "941a983a3d0f4fb9a04161a240719b99"
          ]
        },
        "id": "qy3D0-vsP9f4",
        "outputId": "b4f5217d-7233-410b-8953-05846aea2440"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5b9dbd046e6e48a88d4d1b62e3b8a40d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/2.54k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4de1766addd244b3b1ed5809e37b9cf1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c47d6abcca8a4a00ae6d91e06b8b6f68",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c823ff361ddb45b1beb4ae076d193652",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n",
        "\n",
        "model_id=\"google/flan-t5-base\"\n",
        "\n",
        "# Load tokenizer of FLAN-t5-base\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ujbT1cMXP9f4"
      },
      "source": [
        "before we can start training we need to preprocess our data. Abstractive flan t5 is good for a text2text-generation task. This means our model will take a text as input and generate a text as output. For this we want to understand how long our input and output will be to be able to efficiently batch our data. \n",
        "- as result, we should to convert label from int to string"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R5gXGYlZ0czz"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from datasets import Dataset\n",
        "import random\n",
        "\n",
        "# dataset['train'] = dataset['train'].shuffle(seed=42).select(range(2000)) \n",
        "# dataset['test'] = dataset['test'].shuffle(seed=42).select(range(1000)) \n",
        "\n",
        "dataset['train'] = dataset['train'].shuffle(seed=42)\n",
        "\n",
        "train_df = pd.DataFrame(dataset['train'])\n",
        "test_df = pd.DataFrame(dataset['test'])\n",
        "dataset.clear()\n",
        "train_df['label'] = train_df['label'].astype(str)\n",
        "test_df['label'] = test_df['label'].astype(str)\n",
        "dataset['train'] = Dataset.from_pandas(train_df)\n",
        "dataset['test'] = Dataset.from_pandas(test_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R9LCawlq8twL",
        "outputId": "f515ac6e-5b4e-4293-870c-e6e9a6d5f920"
      },
      "outputs": [
        {
          "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",
              "})"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52,
          "referenced_widgets": [
            "d8b739745b85487d98f3f74da49a9aed",
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            "c9c9d50059d34f0fad398bd102570387",
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            "377683d3d447485ebf673befee53cd13"
          ]
        },
        "id": "exxzuraIc_Zb",
        "outputId": "8ee02460-d8a1-4290-f236-fd257657aa8a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d8b739745b85487d98f3f74da49a9aed",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/50000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Max source length: 512\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "82af238e2fc44909a1508e4f3219a134",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/50000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Max target length: 3\n"
          ]
        }
      ],
      "source": [
        "from datasets import concatenate_datasets\n",
        "\n",
        "# The maximum total input sequence length after tokenization. \n",
        "# Sequences longer than this will be truncated, sequences shorter will be padded.\n",
        "tokenized_inputs = concatenate_datasets([dataset[\"train\"], dataset[\"test\"]]).map(lambda x: tokenizer(x[\"text\"], truncation=True), batched=True, remove_columns=['text', 'label'])\n",
        "max_source_length = max([len(x) for x in tokenized_inputs[\"input_ids\"]])\n",
        "print(f\"Max source length: {max_source_length}\")\n",
        "\n",
        "# The maximum total sequence length for target text after tokenization. \n",
        "# Sequences longer than this will be truncated, sequences shorter will be padded.\"\n",
        "tokenized_targets = concatenate_datasets([dataset[\"train\"], dataset[\"test\"]]).map(lambda x: tokenizer(x[\"label\"], truncation=True), batched=True, remove_columns=['text', 'label'])\n",
        "max_target_length = max([len(x) for x in tokenized_targets[\"input_ids\"]])\n",
        "print(f\"Max target length: {max_target_length}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34,
          "referenced_widgets": [
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            "99bb101ba4094db280d310b88f9faebf",
            "18e91192612c4ecdb805dc1943bbc4a9"
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        },
        "id": "sKKGwPR9P9f5",
        "outputId": "f848b066-f822-40cb-ce5d-b8f8c74cc7e8"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7da81f373baf4708a5208ddb45d22c0a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/25000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f75d111b47bf4e24a051749fa898517a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/25000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Keys of tokenized dataset: ['input_ids', 'attention_mask', 'labels']\n"
          ]
        }
      ],
      "source": [
        "def preprocess_function(sample, padding=\"max_length\"):\n",
        "    # add prefix to the input for t5\n",
        "    inputs = [item for item in sample[\"text\"]]\n",
        "\n",
        "    # tokenize inputs\n",
        "    model_inputs = tokenizer(inputs, max_length=max_source_length, padding=padding, truncation=True)\n",
        "\n",
        "    # Tokenize targets with the `text_target` keyword argument\n",
        "    labels = tokenizer(text_target=sample[\"label\"], max_length=max_target_length, padding=padding, truncation=True)\n",
        "    \n",
        "    # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore\n",
        "    # padding in the loss.\n",
        "    if padding == \"max_length\":\n",
        "        labels[\"input_ids\"] = [\n",
        "            [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels[\"input_ids\"]\n",
        "        ]\n",
        "\n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs\n",
        "\n",
        "tokenized_dataset = dataset.map(preprocess_function, batched=True, remove_columns=['text', 'label'])\n",
        "print(f\"Keys of tokenized dataset: {list(tokenized_dataset['train'].features)}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zEbhOAeCP9f6"
      },
      "source": [
        "## 3. Fine-tune and evaluate FLAN-T5\n",
        "\n",
        "After we have processed our dataset, we can start training our model. Therefore we first need to load our [FLAN-T5](https://huggingface.co/models?search=flan-t5) from the Hugging Face Hub. In the example we are using a instance with a NVIDIA V100 meaning that we will fine-tune the `base` version of the model. \n",
        "_I plan to do a follow-up post on how to fine-tune the `xxl` version of the model using Deepspeed._\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
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            "f655a17abd93442d84c6b00b3cd2bbea",
            "0c8295981dae40768fec70f26105c913",
            "bf5c04f87c8742db86870ccc5e499937",
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            "15380bc39d9a4bc3a61980d8319652bb",
            "43547fe8ad56487aaed4a2833de82e62",
            "6865305bf8244a5ba4358b924395a207",
            "0ebd90cace7e44e89efed86c50464bfd",
            "3647bf848d5e4c27b5d4b2b6b289f149",
            "9c7ef5beca464346b98905595614da9b"
          ]
        },
        "id": "swteeCAoP9f6",
        "outputId": "a4caea5e-b35b-4377-dc35-e8a84fa8462e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2d54c1e365b34069855c05e91fe63521",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.40k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0c8295981dae40768fec70f26105c913",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/990M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1158803fd8ad4e1baaa54215f9389d32",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from transformers import AutoModelForSeq2SeqLM\n",
        "\n",
        "# huggingface hub model id\n",
        "model_id=\"google/flan-t5-base\"\n",
        "\n",
        "# load model from the hub\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_id)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Dp2ZaLjP9f6"
      },
      "source": [
        "We want to evaluate our model during training. The `Trainer` supports evaluation during training by providing a `compute_metrics`.  \n",
        "The most commonly used metrics to evaluate summarization task is [rogue_score](https://en.wikipedia.org/wiki/ROUGE_(metric)) short for Recall-Oriented Understudy for Gisting Evaluation). This metric does not behave like the standard accuracy: it will compare a generated summary against a set of reference summaries\n",
        "\n",
        "We are going to use `evaluate` library to evaluate the `rogue` score."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "be21782cbb514fa48a8bc64b3393eec8",
            "94b80eef329a4ff0835648cf4326dc10",
            "e04ba176394c484a837f02b6226f2223",
            "ad6890b36f1a4d9ba304554476611548",
            "347ac75b46424785b0ab4045dccee1d1",
            "362fe155d9e34cbf9709731b282ed2b5",
            "6e2bffd33ba1451985259d53ba4295da",
            "ef3dba649d504c76bdad67a1646daac3",
            "fbdaf750f5fc418aab0cc1e99b389dd4",
            "82f1dae280234958856065cab407fc7d",
            "dccd30e194ea4b6cbd6fa2261e0bd395"
          ]
        },
        "id": "osGhQTz4P9f7",
        "outputId": "242257bc-dc4c-4a19-a3de-16b45e4eb7cf"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "be21782cbb514fa48a8bc64b3393eec8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.77k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import evaluate\n",
        "import nltk\n",
        "import numpy as np\n",
        "from nltk.tokenize import sent_tokenize\n",
        "nltk.download(\"punkt\")\n",
        "\n",
        "# Metric\n",
        "metric = evaluate.load(\"f1\")\n",
        "\n",
        "# helper function to postprocess text\n",
        "def postprocess_text(preds, labels):\n",
        "    preds = [pred.strip() for pred in preds]\n",
        "    labels = [label.strip() for label in labels]\n",
        "\n",
        "    # rougeLSum expects newline after each sentence\n",
        "    preds = [\"\\n\".join(sent_tokenize(pred)) for pred in preds]\n",
        "    labels = [\"\\n\".join(sent_tokenize(label)) for label in labels]\n",
        "\n",
        "    return preds, labels\n",
        "\n",
        "def compute_metrics(eval_preds):\n",
        "    preds, labels = eval_preds\n",
        "    if isinstance(preds, tuple):\n",
        "        preds = preds[0]\n",
        "    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
        "    # Replace -100 in the labels as we can't decode them.\n",
        "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
        "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
        "\n",
        "    # Some simple post-processing\n",
        "    decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)\n",
        "\n",
        "    result = metric.compute(predictions=decoded_preds, references=decoded_labels, average='macro')\n",
        "    result = {k: round(v * 100, 4) for k, v in result.items()}\n",
        "    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]\n",
        "    result[\"gen_len\"] = np.mean(prediction_lens)\n",
        "    return result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9X0ZrYzTP9f8"
      },
      "source": [
        "Before we can start training is to create a `DataCollator` that will take care of padding our inputs and labels. We will use the `DataCollatorForSeq2Seq` from the 🤗 Transformers library. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hXJvOMTnP9f8"
      },
      "outputs": [],
      "source": [
        "from transformers import DataCollatorForSeq2Seq\n",
        "\n",
        "# we want to ignore tokenizer pad token in the loss\n",
        "label_pad_token_id = -100\n",
        "# Data collator\n",
        "data_collator = DataCollatorForSeq2Seq(\n",
        "    tokenizer,\n",
        "    model=model,\n",
        "    label_pad_token_id=label_pad_token_id,\n",
        "    pad_to_multiple_of=8\n",
        ")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oE_7wq32P9f8"
      },
      "source": [
        "The last step is to define the hyperparameters (`TrainingArguments`) we want to use for our training. We are leveraging the [Hugging Face Hub](https://huggingface.co/models) integration of the `Trainer` to automatically push our checkpoints, logs and metrics during training into a repository."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ZxlsgznP9f9",
        "outputId": "f8729127-cbd8-4389-f103-2cf5bd7df5a1"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Cloning https://huggingface.co/mohammadtaghizadeh/flan-t5-base-imdb-text-classification into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/mohammadtaghizadeh/flan-t5-base-imdb-text-classification into local empty directory.\n"
          ]
        }
      ],
      "source": [
        "from huggingface_hub import HfFolder\n",
        "from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments\n",
        "\n",
        "# Hugging Face repository id\n",
        "repository_id = f\"{model_id.split('/')[1]}-imdb-text-classification\"\n",
        "\n",
        "# Define training args\n",
        "training_args = Seq2SeqTrainingArguments(\n",
        "    output_dir=repository_id,\n",
        "    per_device_train_batch_size=8,\n",
        "    per_device_eval_batch_size=8,\n",
        "    predict_with_generate=True,\n",
        "    fp16=False, # Overflows with fp16\n",
        "    learning_rate=3e-4,\n",
        "\n",
        "    num_train_epochs=2,\n",
        "    # logging & evaluation strategies\n",
        "    logging_dir=f\"{repository_id}/logs\",\n",
        "    logging_strategy=\"epoch\", \n",
        "    # logging_steps=1000,\n",
        "    evaluation_strategy=\"no\",\n",
        "    save_strategy=\"epoch\",\n",
        "    save_total_limit=2,\n",
        "    load_best_model_at_end=False,\n",
        "    # metric_for_best_model=\"overall_f1\",\n",
        "    # push to hub parameters\n",
        "    report_to=\"tensorboard\",\n",
        "    push_to_hub=True,\n",
        "    hub_strategy=\"every_save\",\n",
        "    hub_model_id=repository_id,\n",
        "    hub_token=HfFolder.get_token(),\n",
        ")\n",
        "\n",
        "# Create Trainer instance\n",
        "trainer = Seq2SeqTrainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    data_collator=data_collator,\n",
        "    train_dataset=tokenized_dataset[\"train\"],\n",
        "    eval_dataset=tokenized_dataset[\"test\"],\n",
        "    compute_metrics=compute_metrics,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gTfdbwXfP9f9"
      },
      "source": [
        "We can start our training by using the `train` method of the `Trainer`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        },
        "id": "f8KanGHpP9f-",
        "outputId": "76638732-e657-44dc-a07c-edbb2c37b116"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:391: 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",
            "You're using a T5TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='6250' max='6250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [6250/6250 1:53:05, Epoch 2/2]\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>3125</td>\n",
              "      <td>0.100500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6250</td>\n",
              "      <td>0.043600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=6250, training_loss=0.072074638671875, metrics={'train_runtime': 6789.0465, 'train_samples_per_second': 7.365, 'train_steps_per_second': 0.921, 'total_flos': 3.42378676224e+16, 'train_loss': 0.072074638671875, 'epoch': 2.0})"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Start training \n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rdi_pJLBP9f-"
      },
      "source": [
        "Nice, we have trained our model. 🎉 Lets run evaluate the best model again on the test set.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158
        },
        "id": "x_RRbvvoP9f-",
        "outputId": "6fc08fa5-4bf2-4bb1-8d71-9a23a6864422"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='3125' max='3125' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [3125/3125 39:50]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "{'eval_loss': 0.07672297954559326,\n",
              " 'eval_f1': 95.084,\n",
              " 'eval_gen_len': 2.49756,\n",
              " 'eval_runtime': 2396.5247,\n",
              " 'eval_samples_per_second': 10.432,\n",
              " 'eval_steps_per_second': 1.304,\n",
              " 'epoch': 2.0}"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.evaluate()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "id": "EgKyYOXWP9gA",
        "outputId": "a1702156-2380-42f4-c339-0304e14f9832"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "To https://huggingface.co/mohammadtaghizadeh/flan-t5-base-imdb-text-classification\n",
            "   d6dd496..887480e  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/mohammadtaghizadeh/flan-t5-base-imdb-text-classification\n",
            "   d6dd496..887480e  main -> main\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://huggingface.co/mohammadtaghizadeh/flan-t5-base-imdb-text-classification/commit/887480eac614cd8f821f14e804ebccd2a4a75457'"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Save our tokenizer and create model card\n",
        "tokenizer.save_pretrained(repository_id)\n",
        "trainer.create_model_card()\n",
        "# Push the results to the hub\n",
        "trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sRCjQfZ8B1p8"
      },
      "source": [
        "## 4. Run Inference and Classification Report"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm.auto import tqdm\n",
        "\n",
        "samples_number = len(dataset['test'])\n",
        "progress_bar = tqdm(range(samples_number))\n",
        "predictions_list = []\n",
        "labels_list = []\n",
        "for i in range(samples_number):\n",
        "  text = dataset['test']['text'][i]\n",
        "  inputs = tokenizer.encode_plus(text, padding='max_length', max_length=512, return_tensors='pt').to('cuda')\n",
        "  outputs = model.generate(inputs['input_ids'], attention_mask=inputs['attention_mask'], max_length=150, num_beams=4, early_stopping=True)\n",
        "  prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
        "  predictions_list.append(prediction)\n",
        "  labels_list.append(dataset['test']['label'][i])\n",
        "\n",
        "  progress_bar.update(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "46a8398ea49d4ea7b81f6cc6608d4f43",
            "65a848dced51484f99697ead0d5de20f",
            "6bc72cbf66cb465299bd223d07af0144",
            "c3872fa35be34c72b155d64e4619bd72",
            "aaede0c5836e491295f93d7740fe9bd9",
            "aa8dea242d63462ca6471e9c40cac58e",
            "9ee25867d8174836bd2ccbd0db057843",
            "f5ce8d20f0704cd5a34a715ec561d10f",
            "79e9adec779b4bdc8f5222b799e83b3a",
            "2f1c776244034d3489d3fed00953c130",
            "2d027164f66543d7acf1c439ef4ed998"
          ]
        },
        "outputId": "0ae028c2-589a-4963-9e1a-4e4c0a804a08",
        "id": "2aP6bVVfmOhf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/25000 [00:00<?, ?it/s]"
            ],
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              "version_major": 2,
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "str_labels_list = []\n",
        "for i in range(len(labels_list)): str_labels_list.append(str(labels_list[i]))"
      ],
      "metadata": {
        "id": "XCU7WxZWkjU1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "report = classification_report(str_labels_list, predictions_list, zero_division=0)\n",
        "print(report)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1cb726c0-079e-49bf-e4ec-28f8e62d4771",
        "id": "X3iqrE4CmOhl"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.88      0.92     12500\n",
            "           1       0.89      0.97      0.93     12500\n",
            "\n",
            "    accuracy                           0.93     25000\n",
            "   macro avg       0.93      0.93      0.93     25000\n",
            "weighted avg       0.93      0.93      0.93     25000\n",
            "\n"
          ]
        }
      ]
    }
  ],
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