t5_qg (1).ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Download the dependancies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IEcrlZEG4Td6",
        "outputId": "70e2f18b-5e82-43d5-96c0-271465f4e98e"
      },
      "outputs": [],
      "source": [
        "!pip install datasets\n",
        "!pip install transformers\n",
        "!pip install accelerate -U\n",
        "!pip install evaluate\n",
        "!pip install torch torchvision torchaudio\n",
        "!pip install scikit-learn -U\n",
        "!pip install sentencepiece"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Load the dataset"
      ]
    },
    {
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      "execution_count": null,
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        "colab": {
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        "outputId": "e1768ef0-2ab4-4579-cc96-63c6e4a20032"
      },
      "outputs": [],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "# Load the dataset\n",
        "dataset = load_dataset(\"squad_v2\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Load the model and the tokenizer"
      ]
    },
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        "outputId": "d7b95442-62b4-41a5-8f73-88196c3e1d3e"
      },
      "outputs": [],
      "source": [
        "from transformers import T5ForConditionalGeneration, T5Tokenizer, Trainer, TrainingArguments\n",
        "\n",
        "tokenizer = T5Tokenizer.from_pretrained(\"t5-small\")\n",
        "model = T5ForConditionalGeneration.from_pretrained(\"t5-small\", model_max_length=512)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Run the tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
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        "outputId": "1c0cfc39-3694-498b-f14a-2ebc4452d68f"
      },
      "outputs": [],
      "source": [
        "def tokenize_example(example):\n",
        "\n",
        "    model_input = tokenizer(f\"question: {example['question']} context: {example['context']}\",\n",
        "                            padding=\"max_length\", truncation=True, max_length=512)\n",
        "\n",
        "    # Tokenize the answer\n",
        "    with tokenizer.as_target_tokenizer():\n",
        "\n",
        "        label = tokenizer(f\"{example['answers']['text']}\", padding=\"max_length\", truncation=True, max_length=128)\n",
        "    \n",
        "    model_input[\"labels\"] = label[\"input_ids\"]\n",
        "    return model_input\n",
        "\n",
        "# test part of the dataset\n",
        "train_dataset = dataset[\"train\"].select(range(5000))\n",
        "eval_dataset = dataset[\"validation\"].select(range(5000))\n",
        "\n",
        "tokenized_train_dataset = [tokenize_example(example) for example in train_dataset]\n",
        "tokenized_eval_dataset = [tokenize_example(example) for example in eval_dataset]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Set the training args and the transformer trainer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O0kDmWMS5HWP"
      },
      "outputs": [],
      "source": [
        "from transformers import TrainingArguments, Trainer\n",
        "\n",
        "training_args = TrainingArguments(output_dir=\"test_trainer\", evaluation_strategy=\"epoch\",fp16=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dysRONPs5I0_"
      },
      "outputs": [],
      "source": [
        "from transformers import TrainingArguments, Trainer\n",
        "\n",
        "training_args = TrainingArguments(output_dir=\"test_trainer\", evaluation_strategy=\"epoch\",fp16=True) #fp16 is set to True to use mixed precision training\n",
        "\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_train_dataset,\n",
        "    eval_dataset=tokenized_eval_dataset,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "YPOFMcXJ5LsL",
        "outputId": "40277b06-30f6-4401-cf66-003730888474"
      },
      "outputs": [],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Save the model to disk"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NrmMM4dFTRJi"
      },
      "outputs": [],
      "source": [
        "model.save_pretrained(\"./t5-qg_v2\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Run inference"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F0pe6_TnTfUZ",
        "outputId": "8f690be7-c4a8-486b-f5a4-5adbe56c9951"
      },
      "outputs": [],
      "source": [
        "context = \"Rick and Morty is an American adult animated science fiction sitcom created by Justin Roiland and Dan Harmon for Cartoon Network's nighttime programming block Adult Swim. The series follows the misadventures of Rick Sanchez, a cynical mad scientist, and his good-hearted but fretful grandson Morty Smith, who split their time between domestic life and interdimensional adventures that take place across an infinite number of realities, often traveling to other planets and dimensions through portals and on Rick's flying saucer.\"\n",
        "model = T5ForConditionalGeneration.from_pretrained(\"./t5-qg_v2\")\n",
        "tokenizer = T5Tokenizer.from_pretrained(\"t5-base\")\n",
        "input_ids = tokenizer(\"carefully analyze the text. generate question from key information: \" + context, return_tensors=\"pt\").input_ids\n",
        "outputs = model.generate(input_ids)\n",
        "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
      ]
    }
  ],
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