1.11.24_04_09_adv_nlp_end.ipynb.ipynb

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        "<a href=\"https://colab.research.google.com/github/gstripling/Generative_AI/blob/main/1.11.24_04_09_adv_nlp_end.ipynb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "#QUESTION ANSWERING"
      ],
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      "source": [
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        "from transformers import pipeline, DistilBertTokenizer, DistilBertForQuestionAnswering\n",
        "\n",
        "# Load pre-trained DistilBERT model and tokenizer\n",
        "model = DistilBertForQuestionAnswering.from_pretrained('distilbert-base-uncased-distilled-squad')\n",
        "tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased-distilled-squad')\n",
        "\n",
        "# Example context and question\n",
        "context = \"DistilBERT is a lightweight version of BERT designed for speed and efficiency.\"\n",
        "question = \"What is DistilBERT designed for?\"\n",
        "\n",
        "# Tokenize and encode the context and question\n",
        "inputs = tokenizer(context, question, return_tensors='pt')\n",
        "\n",
        "# Get the model's output\n",
        "outputs = model(**inputs)\n",
        "\n",
        "# Extract start and end logits from the output\n",
        "start_logits = outputs.start_logits\n",
        "end_logits = outputs.end_logits\n",
        "\n",
        "# Use the tokenizer to convert logits to tokens\n",
        "start_index = start_logits.argmax(dim=-1).item()\n",
        "end_index = end_logits.argmax(dim=-1).item()\n",
        "tokens = tokenizer.convert_ids_to_tokens(inputs['input_ids'][0][start_index:end_index + 1])\n",
        "\n",
        "# Convert tokens back to text\n",
        "answer = tokenizer.convert_tokens_to_string(tokens)\n",
        "\n",
        "# Print the question-answering result\n",
        "print(\"Question:\", question)\n",
        "print(\"Answer:\", answer)\n"
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            "Question: What is DistilBERT designed for?\n",
            "Answer: speed and efficiency\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the above:\n",
        "In this example:\n",
        "\n",
        "We load a pre-trained DistilBERT model and tokenizer specifically fine-tuned for question-answering tasks (distilbert-base-uncased-distilled-squad).\n",
        "\n",
        "We provide a context (a piece of text) and a question related to that context.\n",
        "\n",
        "We tokenize and encode the context and question using the DistilBERT tokenizer.\n",
        "\n",
        "We pass the encoded inputs through the DistilBERT model to obtain start and end logits. These logits represent the probability distribution over tokens for the start and end positions of the answer.\n",
        "\n",
        "We use the tokenizer to convert logits to tokens and then convert the tokens back to text to form the final answer.\n",
        "\n",
        "The question and the obtained answer are printed.\n",
        "\n",
        "You can customize this example by providing your own context and question to perform question-answering on topics of interest to you."
      ],
      "metadata": {
        "id": "19oVrKMKwBwa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "ogeLeuHbwqcw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Named Entity Extraction"
      ],
      "metadata": {
        "id": "_U1DUUsLwqnR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline, DistilBertTokenizer, DistilBertForTokenClassification\n",
        "\n",
        "# Load pre-trained DistilBERT model and tokenizer for token classification\n",
        "model = DistilBertForTokenClassification.from_pretrained('distilbert-base-uncased')\n",
        "tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')\n",
        "\n",
        "# Example text for named entity recognition\n",
        "text = \"Apple Inc. was founded by Steve Jobs, Steve Wozniak, and Ronald Wayne.\"\n",
        "\n",
        "# Tokenize and encode the text\n",
        "tokens = tokenizer(text, return_tensors='pt')\n",
        "\n",
        "# Get the model's output\n",
        "outputs = model(**tokens)\n",
        "\n",
        "# Extract predicted labels (named entities) from the output\n",
        "predicted_labels = outputs.logits.argmax(dim=2).squeeze().numpy()\n",
        "\n",
        "# Map labels back to their corresponding tokens using the tokenizer\n",
        "predicted_tokens = tokenizer.convert_ids_to_tokens(tokens['input_ids'][0].numpy())\n",
        "predicted_entities = [tokenizer.convert_ids_to_tokens([token_id])[0] for token_id in predicted_labels]\n",
        "\n",
        "# Extract named entities from the predicted labels and tokens\n",
        "#named_entities = [(token, entity) for token, entity_id in zip(predicted_tokens, predicted_entities) if entity_id.startswith('B-')]\n",
        "named_entities = [(token, entity) for token, entity_id in zip(text[0].split(), predicted_entities[0]) if entity_id != '[PAD]']\n",
        "\n",
        "\n",
        "# Print the named entities\n",
        "print(\"Named Entities:\")\n",
        "for token, entity in named_entities:\n",
        "    print(f\"{token}: {entity}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "BeEbd7Ihws-P",
        "outputId": "721593fe-35d2-480d-ba5b-69da795b61e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of DistilBertForTokenClassification were not initialized from the model checkpoint at distilbert-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"
          ]
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'entity' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-61-9c6f8cf0ffd2>\u001b[0m in \u001b[0;36m<cell line: 25>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;31m# Extract named entities from the predicted labels and tokens\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;31m#named_entities = [(token, entity) for token, entity_id in zip(predicted_tokens, predicted_entities) if entity_id.startswith('B-')]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0mnamed_entities\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mentity\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtoken\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mentity_id\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredicted_entities\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mentity_id\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'[PAD]'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-61-9c6f8cf0ffd2>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;31m# Extract named entities from the predicted labels and tokens\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;31m#named_entities = [(token, entity) for token, entity_id in zip(predicted_tokens, predicted_entities) if entity_id.startswith('B-')]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0mnamed_entities\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mentity\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtoken\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mentity_id\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredicted_entities\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mentity_id\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'[PAD]'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'entity' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "DHrL7D7zylCf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#NER"
      ],
      "metadata": {
        "id": "1VyP2pt0ylGj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#pip install transformers\n"
      ],
      "metadata": {
        "id": "1QqXzXCzynBU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Apple    B-ORG\n",
        "Inc.     I-ORG\n",
        "was      O\n",
        "founded  O\n",
        "by       O\n",
        "Steve    B-PER\n",
        "Jobs,    I-PER\n",
        "Steve    B-PER\n",
        "Wozniak, I-PER\n",
        "and      O\n",
        "Ronald   B-PER\n",
        "Wayne.   I-PER\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "GBdni00xyw_1",
        "outputId": "d5e4b69c-933a-474b-ff3c-32e1ea52c068"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "invalid syntax (<ipython-input-56-4871fc2fa3ae>, line 1)",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-56-4871fc2fa3ae>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    Apple    B-ORG\u001b[0m\n\u001b[0m             ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "o3ThR7q60Vf4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#TEXT GENERATION\n",
        "\n"
      ],
      "metadata": {
        "id": "wNCWQy180Viu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "DistilBERT, as a distilled version of BERT, is primarily designed for tasks like question answering, text classification, and named entity recognition. It may not be the most suitable model for text generation, as its architecture is optimized for understanding context and relationships in input text rather than generating new content.\n",
        "\n",
        "However, for text generation tasks, models like GPT (Generative Pre-trained Transformer) are more commonly used. Since DistilBERT doesn't have a generative architecture, I'll provide you with an example using GPT-2 for text generation:"
      ],
      "metadata": {
        "id": "hfpXjt4x1EOU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers"
      ],
      "metadata": {
        "id": "smfJQ9pH1BU-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import GPT2LMHeadModel, GPT2Tokenizer\n",
        "\n",
        "# Load pre-trained GPT-2 model and tokenizer for text generation\n",
        "model = GPT2LMHeadModel.from_pretrained('gpt2')\n",
        "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
        "\n",
        "# Prompt for text generation\n",
        "prompt = \"Once upon a time in a land far, far away\"\n",
        "\n",
        "# Tokenize and encode the prompt\n",
        "input_ids = tokenizer.encode(prompt, return_tensors='pt')\n",
        "\n",
        "# Generate text using the model\n",
        "output = model.generate(input_ids, max_length=100, num_return_sequences=1, no_repeat_ngram_size=2, top_k=50, top_p=0.95, temperature=0.7)\n",
        "\n",
        "# Decode and print the generated text\n",
        "generated_text = tokenizer.decode(output[0], skip_special_tokens=True)\n",
        "print(\"Generated Text:\")\n",
        "print(generated_text)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402,
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        "outputId": "dc746621-ade4-486b-cd3e-7b35ac0dff2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
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              "model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]"
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            "text/plain": [
              "generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
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              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:381: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.7` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:386: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n",
            "  warnings.warn(\n",
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generated Text:\n",
            "Once upon a time in a land far, far away, the world was a place of great beauty and great danger. The world of the gods was the land of darkness and darkness. And the darkness of this world, which was far from the light of day, was not the place where the sun and the moon met. It was in the midst of all the worlds, and it was there that the stars and all that were in them met, that they were all in one place.\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this example, I've used GPT-2 for text generation. You can customize the prompt variable to set the initial context for the generated text. Feel free to experiment with the parameters of the model.generate function to adjust the length, diversity, and other aspects of the generated text.\n",
        "\n",
        "Please note that GPT-2 is more suitable for text generation tasks, whereas DistilBERT is better suited for tasks like classification, question answering, and named entity recognition. If you specifically need text generation capabilities, GPT-2 or similar models would be more appropriate."
      ],
      "metadata": {
        "id": "LxTg-VJu1VF8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#MULTIPLE PROMPTS"
      ],
      "metadata": {
        "id": "PMAOCFVr1iCS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# NEW EXERCISE\n",
        "from transformers import GPT2LMHeadModel, GPT2Tokenizer\n",
        "\n",
        "# Load pre-trained GPT-2 model and tokenizer for text generation\n",
        "model = GPT2LMHeadModel.from_pretrained('gpt2')\n",
        "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
        "\n",
        "# List of prompts for text generation\n",
        "prompts = [\"Once upon a time in a land far, far away\",\n",
        "           \"In a galaxy not so distant, there was a hero\",\n",
        "           \"The mysterious door creaked open revealing\"]\n",
        "\n",
        "# Generate text for each prompt\n",
        "for prompt in prompts:\n",
        "    # Tokenize and encode the prompt\n",
        "    input_ids = tokenizer.encode(prompt, return_tensors='pt')\n",
        "\n",
        "    # Generate text using the model\n",
        "    output = model.generate(input_ids, max_length=100, num_return_sequences=1, no_repeat_ngram_size=2, top_k=50, top_p=0.95, temperature=0.7)\n",
        "\n",
        "    # Decode and print the generated text\n",
        "    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)\n",
        "\n",
        "    print(f\"Prompt: {prompt}\")\n",
        "    print(\"Generated Text:\")\n",
        "    print(generated_text)\n",
        "    print(\"\\n\" + \"=\"*50 + \"\\n\")  # Separator between generated texts\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q3nhG_gx1V1Q",
        "outputId": "7964f1f3-fb2e-461b-82cf-8441864845d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: Once upon a time in a land far, far away\n",
            "Generated Text:\n",
            "Once upon a time in a land far, far away, the world was a place of great beauty and great danger. The world of the gods was the land of darkness and darkness. And the darkness of this world, which was far from the light of day, was not the place where the sun and the moon met. It was in the midst of all the worlds, and it was there that the stars and all that were in them met, that they were all in one place.\n",
            "\n",
            "\n",
            "\n",
            "==================================================\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: In a galaxy not so distant, there was a hero\n",
            "Generated Text:\n",
            "In a galaxy not so distant, there was a hero who could save the galaxy.\n",
            "\n",
            "The hero was the hero of the Galactic Empire. He was known as the Emperor. The Emperor was an Emperor who had been born in the past, and had become a Jedi. His name was Darth Vader. Vader was born on the planet of Coruscant, where he was raised by his father, Darth Sidious. After his birth, Vader became a Sith Lord, a master of Sith sorcery.\n",
            "\n",
            "==================================================\n",
            "\n",
            "Prompt: The mysterious door creaked open revealing\n",
            "Generated Text:\n",
            "The mysterious door creaked open revealing a man in a black suit and a white shirt. He was wearing a red hooded sweatshirt and black pants.\n",
            "\n",
            "\"I'm a doctor,\" he said. \"I've been in the hospital for a year. I've never seen anything like this.\"\n",
            "...\n",
            ", a former nurse who worked at the University of California, Berkeley, and was a member of the medical staff at UC Berkeley's medical center. She was killed in an\n",
            "\n",
            "==================================================\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "tPmtx0wi8wBi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Fine Tuning LLM on Domain Specific Data"
      ],
      "metadata": {
        "id": "5Tump_GR8wE9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import GPT2LMHeadModel, GPT2Tokenizer, GPT2Config\n",
        "from transformers import TextDataset, DataCollatorForLanguageModeling\n",
        "from transformers import Trainer, TrainingArguments\n",
        "\n",
        "# Load pre-trained GPT-2 model and tokenizer\n",
        "model = GPT2LMHeadModel.from_pretrained('gpt2')\n",
        "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
        "\n",
        "# Load your domain-specific data\n",
        "train_dataset = TextDataset(\n",
        "    tokenizer=tokenizer,\n",
        "    file_path='/content/advertising.csv',\n",
        "   # file_path='path/to/your/train.txt',\n",
        "    block_size=128\n",
        ")\n",
        "\n",
        "\n",
        "\n",
        "# Define data collator\n",
        "data_collator = DataCollatorForLanguageModeling(\n",
        "    tokenizer=tokenizer,\n",
        "    mlm=False\n",
        ")\n",
        "\n",
        "# Define training arguments\n",
        "training_args = TrainingArguments(\n",
        "    output_dir='output_directory',\n",
        "    overwrite_output_dir=True,\n",
        "    num_train_epochs=3,\n",
        "    per_device_train_batch_size=8,\n",
        "    save_steps=10_000,\n",
        "    save_total_limit=2,\n",
        ")\n",
        "\n",
        "# Initialize Trainer\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    data_collator=data_collator,\n",
        "    train_dataset=train_dataset,\n",
        ")\n",
        "\n",
        "# Fine-tune the model\n",
        "trainer.train()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "id": "3eIXRdSK8y6-",
        "outputId": "ec1f39d8-9836-4927-e164-22d613b7a39f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/data/datasets/language_modeling.py:53: FutureWarning: This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets library. You can have a look at this example script for pointers: https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ImportError",
          "evalue": "Using the `Trainer` with `PyTorch` requires `accelerate>=0.20.1`: Please run `pip install transformers[torch]` or `pip install accelerate -U`",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-68-69a0b5bfeacc>\u001b[0m in \u001b[0;36m<cell line: 26>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;31m# Define training arguments\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m training_args = TrainingArguments(\n\u001b[0m\u001b[1;32m     27\u001b[0m     \u001b[0moutput_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'output_directory'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0moverwrite_output_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/training_args.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, output_dir, overwrite_output_dir, do_train, do_eval, do_predict, evaluation_strategy, prediction_loss_only, per_device_train_batch_size, per_device_eval_batch_size, per_gpu_train_batch_size, per_gpu_eval_batch_size, gradient_accumulation_steps, eval_accumulation_steps, eval_delay, learning_rate, weight_decay, adam_beta1, adam_beta2, adam_epsilon, max_grad_norm, num_train_epochs, max_steps, lr_scheduler_type, warmup_ratio, warmup_steps, log_level, log_level_replica, log_on_each_node, logging_dir, logging_strategy, logging_first_step, logging_steps, logging_nan_inf_filter, save_strategy, save_steps, save_total_limit, save_safetensors, save_on_each_node, no_cuda, use_cpu, use_mps_device, seed, data_seed, jit_mode_eval, use_ipex, bf16, fp16, fp16_opt_level, half_precision_backend, bf16_full_eval, fp16_full_eval, tf32, local_rank, ddp_backend, tpu_num_cores, tpu_metrics_debug, debug, dataloader_drop_last, eval_steps, dataloader_num_workers, past_index, run_name, disable_tqdm, remove_unused_columns, label_names, load_best_model_at_end, metric_for_best_model, greater_is_better, ignore_data_skip, fsdp, fsdp_min_num_params, fsdp_config, fsdp_transformer_layer_cls_to_wrap, deepspeed, label_smoothing_factor, optim, optim_args, adafactor, group_by_length, length_column_name, report_to, ddp_find_unused_parameters, ddp_bucket_cap_mb, ddp_broadcast_buffers, dataloader_pin_memo...\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/training_args.py\u001b[0m in \u001b[0;36m__post_init__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1440\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"pt\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mis_torch_available\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1442\u001b[0;31m             \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"cuda\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1443\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"npu\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1444\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"xpu\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/training_args.py\u001b[0m in \u001b[0;36mdevice\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1885\u001b[0m         \"\"\"\n\u001b[1;32m   1886\u001b[0m         \u001b[0mrequires_backends\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"torch\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1887\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_devices\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1888\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1889\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/utils/generic.py\u001b[0m in \u001b[0;36m__get__\u001b[0;34m(self, obj, objtype)\u001b[0m\n\u001b[1;32m     52\u001b[0m         \u001b[0mcached\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcached\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m             \u001b[0mcached\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     55\u001b[0m             \u001b[0msetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcached\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcached\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/training_args.py\u001b[0m in \u001b[0;36m_setup_devices\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1785\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_sagemaker_mp_enabled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1786\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_accelerate_available\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin_version\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"0.20.1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1787\u001b[0;31m                 raise ImportError(\n\u001b[0m\u001b[1;32m   1788\u001b[0m                     \u001b[0;34m\"Using the `Trainer` with `PyTorch` requires `accelerate>=0.20.1`: Please run `pip install transformers[torch]` or `pip install accelerate -U`\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1789\u001b[0m                 )\n",
            "\u001b[0;31mImportError\u001b[0m: Using the `Trainer` with `PyTorch` requires `accelerate>=0.20.1`: Please run `pip install transformers[torch]` or `pip install accelerate -U`",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ImportError: Using the `Trainer` with `PyTorch` requires `accelerate>=0.20.1`: Please run `pip install transformers[torch]` or `pip install accelerate -U`"
      ],
      "metadata": {
        "id": "zGhweVti9WUr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install transformers[torch]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m-eKTBEl9XaP",
        "outputId": "91b903f2-109a-4a15-e3a4-0af06de214ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers[torch] in /usr/local/lib/python3.10/dist-packages (4.35.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (3.13.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.20.2)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (23.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.15.0)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.4.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (4.66.1)\n",
            "Requirement already satisfied: torch!=1.12.0,>=1.10 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (2.1.0+cu121)\n",
            "Collecting accelerate>=0.20.3 (from transformers[torch])\n",
            "  Downloading accelerate-0.26.0-py3-none-any.whl (270 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m270.7/270.7 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.20.3->transformers[torch]) (5.9.5)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers[torch]) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers[torch]) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.10->transformers[torch]) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.10->transformers[torch]) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.10->transformers[torch]) (3.1.2)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.10->transformers[torch]) (2.1.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (2023.11.17)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch!=1.12.0,>=1.10->transformers[torch]) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch!=1.12.0,>=1.10->transformers[torch]) (1.3.0)\n",
            "Installing collected packages: accelerate\n",
            "Successfully installed accelerate-0.26.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install accelerate -U"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LvVNLQmI9mfs",
        "outputId": "efc9b0f0-b145-43f8-ef86-0bf4ddfa2df5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.26.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (23.2)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.1)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.1.0+cu121)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.20.2)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.4.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.13.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2023.6.0)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.1.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.66.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2023.11.17)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import GPT2LMHeadModel, GPT2Tokenizer, GPT2Config\n",
        "from transformers import TextDataset, DataCollatorForLanguageModeling\n",
        "from transformers import Trainer, TrainingArguments\n",
        "\n",
        "# Load pre-trained GPT-2 model and tokenizer\n",
        "model = GPT2LMHeadModel.from_pretrained('gpt2')\n",
        "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
        "\n",
        "# Load your domain-specific data\n",
        "train_dataset = TextDataset(\n",
        "    tokenizer=tokenizer,\n",
        "    file_path='/content/advertising.csv',\n",
        "   # file_path='path/to/your/train.txt',\n",
        "    block_size=128\n",
        ")\n",
        "\n",
        "\n",
        "\n",
        "# Define data collator\n",
        "data_collator = DataCollatorForLanguageModeling(\n",
        "    tokenizer=tokenizer,\n",
        "    mlm=False\n",
        ")\n",
        "\n",
        "# Define training arguments\n",
        "training_args = TrainingArguments(\n",
        "    output_dir='output_directory',\n",
        "    overwrite_output_dir=True,\n",
        "    num_train_epochs=3,\n",
        "    per_device_train_batch_size=8,\n",
        "    save_steps=10_000,\n",
        "    save_total_limit=2,\n",
        ")\n",
        "\n",
        "# Initialize Trainer\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    data_collator=data_collator,\n",
        "    train_dataset=train_dataset,\n",
        ")\n",
        "\n",
        "# Fine-tune the model\n",
        "trainer.train()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        },
        "id": "fL7RITUK9dup",
        "outputId": "a83d47d3-f369-48b9-a3ef-3a960fcc039b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ImportError",
          "evalue": "Using the `Trainer` with `PyTorch` requires `accelerate>=0.20.1`: Please run `pip install transformers[torch]` or `pip install accelerate -U`",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-72-69a0b5bfeacc>\u001b[0m in \u001b[0;36m<cell line: 26>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;31m# Define training arguments\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m training_args = TrainingArguments(\n\u001b[0m\u001b[1;32m     27\u001b[0m     \u001b[0moutput_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'output_directory'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0moverwrite_output_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/training_args.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, output_dir, overwrite_output_dir, do_train, do_eval, do_predict, evaluation_strategy, prediction_loss_only, per_device_train_batch_size, per_device_eval_batch_size, per_gpu_train_batch_size, per_gpu_eval_batch_size, gradient_accumulation_steps, eval_accumulation_steps, eval_delay, learning_rate, weight_decay, adam_beta1, adam_beta2, adam_epsilon, max_grad_norm, num_train_epochs, max_steps, lr_scheduler_type, warmup_ratio, warmup_steps, log_level, log_level_replica, log_on_each_node, logging_dir, logging_strategy, logging_first_step, logging_steps, logging_nan_inf_filter, save_strategy, save_steps, save_total_limit, save_safetensors, save_on_each_node, no_cuda, use_cpu, use_mps_device, seed, data_seed, jit_mode_eval, use_ipex, bf16, fp16, fp16_opt_level, half_precision_backend, bf16_full_eval, fp16_full_eval, tf32, local_rank, ddp_backend, tpu_num_cores, tpu_metrics_debug, debug, dataloader_drop_last, eval_steps, dataloader_num_workers, past_index, run_name, disable_tqdm, remove_unused_columns, label_names, load_best_model_at_end, metric_for_best_model, greater_is_better, ignore_data_skip, fsdp, fsdp_min_num_params, fsdp_config, fsdp_transformer_layer_cls_to_wrap, deepspeed, label_smoothing_factor, optim, optim_args, adafactor, group_by_length, length_column_name, report_to, ddp_find_unused_parameters, ddp_bucket_cap_mb, ddp_broadcast_buffers, dataloader_pin_memo...\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/training_args.py\u001b[0m in \u001b[0;36m__post_init__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1440\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"pt\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mis_torch_available\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1442\u001b[0;31m             \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"cuda\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1443\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"npu\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1444\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"xpu\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/training_args.py\u001b[0m in \u001b[0;36mdevice\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1885\u001b[0m         \"\"\"\n\u001b[1;32m   1886\u001b[0m         \u001b[0mrequires_backends\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"torch\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1887\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_devices\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1888\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1889\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/utils/generic.py\u001b[0m in \u001b[0;36m__get__\u001b[0;34m(self, obj, objtype)\u001b[0m\n\u001b[1;32m     52\u001b[0m         \u001b[0mcached\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcached\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m             \u001b[0mcached\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     55\u001b[0m             \u001b[0msetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcached\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcached\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/training_args.py\u001b[0m in \u001b[0;36m_setup_devices\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1785\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_sagemaker_mp_enabled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1786\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_accelerate_available\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin_version\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"0.20.1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1787\u001b[0;31m                 raise ImportError(\n\u001b[0m\u001b[1;32m   1788\u001b[0m                     \u001b[0;34m\"Using the `Trainer` with `PyTorch` requires `accelerate>=0.20.1`: Please run `pip install transformers[torch]` or `pip install accelerate -U`\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1789\u001b[0m                 )\n",
            "\u001b[0;31mImportError\u001b[0m: Using the `Trainer` with `PyTorch` requires `accelerate>=0.20.1`: Please run `pip install transformers[torch]` or `pip install accelerate -U`",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow transformers\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d_HcWjkE-Amn",
        "outputId": "d1a04e64-7804-49aa-e9b2-69427e75d2f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.15.0)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TFGPT2LMHeadModel, GPT2Tokenizer, GPT2Config\n",
        "from transformers import TextDataset, DataCollatorForLanguageModeling\n",
        "from transformers import TFTrainer, TFTrainingArguments\n",
        "\n",
        "# Load pre-trained GPT-2 model and tokenizer\n",
        "model = TFGPT2LMHeadModel.from_pretrained('gpt2')\n",
        "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
        "\n",
        "# Load your domain-specific data\n",
        "train_dataset = TextDataset(\n",
        "    tokenizer=tokenizer,\n",
        "    file_path='/content/advertising.csv',\n",
        "    block_size=128\n",
        ")\n",
        "\n",
        "# Define data collator\n",
        "data_collator = DataCollatorForLanguageModeling(\n",
        "    tokenizer=tokenizer,\n",
        "    mlm=False\n",
        ")\n",
        "\n",
        "# Define training arguments\n",
        "training_args = TFTrainingArguments(\n",
        "    output_dir='output_directory',\n",
        "    overwrite_output_dir=True,\n",
        "    num_train_epochs=3,\n",
        "    per_device_train_batch_size=8,\n",
        "    save_steps=10_000,\n",
        "    save_total_limit=2,\n",
        ")\n",
        "\n",
        "# Initialize Trainer\n",
        "trainer = TFTrainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    data_collator=data_collator,\n",
        "    train_dataset=train_dataset,\n",
        ")\n",
        "\n",
        "# Fine-tune the model\n",
        "trainer.train()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "LBcHlQR3-DMR",
        "outputId": "ab752150-ff75-40ee-892a-104be0226293"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All PyTorch model weights were used when initializing TFGPT2LMHeadModel.\n",
            "\n",
            "All the weights of TFGPT2LMHeadModel were initialized from the PyTorch model.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFGPT2LMHeadModel for predictions without further training.\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "TFTrainer.__init__() got an unexpected keyword argument 'data_collator'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-74-a4d166f6e90a>\u001b[0m in \u001b[0;36m<cell line: 33>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;31m# Initialize Trainer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m trainer = TFTrainer(\n\u001b[0m\u001b[1;32m     34\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m     \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining_args\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: TFTrainer.__init__() got an unexpected keyword argument 'data_collator'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TFGPT2LMHeadModel, GPT2Tokenizer, GPT2Config\n",
        "from transformers import TextDataset, DataCollatorForLanguageModeling\n",
        "from transformers import TFTrainer, TFTrainingArguments\n",
        "\n",
        "# Load pre-trained GPT-2 model and tokenizer\n",
        "model = TFGPT2LMHeadModel.from_pretrained('gpt2')\n",
        "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
        "\n",
        "# Load your domain-specific data\n",
        "train_dataset = TextDataset(\n",
        "    tokenizer=tokenizer,\n",
        "      file_path='/content/advertising.csv',\n",
        "    block_size=128\n",
        ")\n",
        "\n",
        "# Define data collator\n",
        "data_collator = DataCollatorForLanguageModeling(\n",
        "    tokenizer=tokenizer,\n",
        "    mlm=False\n",
        ")\n",
        "\n",
        "# Define training arguments\n",
        "training_args = TFTrainingArguments(\n",
        "    output_dir='output_directory',\n",
        "    overwrite_output_dir=True,\n",
        "    num_train_epochs=3,\n",
        "    per_device_train_batch_size=8,\n",
        "    save_steps=10_000,\n",
        "    save_total_limit=2,\n",
        "    prediction_loss_only=True,  # This ensures only the prediction loss is computed (equivalent to mlm=False)\n",
        ")\n",
        "\n",
        "# Initialize Trainer\n",
        "trainer = TFTrainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    data_collator=data_collator,\n",
        ")\n",
        "\n",
        "# Fine-tune the model\n",
        "trainer.train()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "5abOIith-ed6",
        "outputId": "1f2f2409-cf04-41e4-ce9a-d25f194703ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All PyTorch model weights were used when initializing TFGPT2LMHeadModel.\n",
            "\n",
            "All the weights of TFGPT2LMHeadModel were initialized from the PyTorch model.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFGPT2LMHeadModel for predictions without further training.\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "TFTrainer.__init__() got an unexpected keyword argument 'data_collator'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-75-d2840edb0201>\u001b[0m in \u001b[0;36m<cell line: 34>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;31m# Initialize Trainer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m trainer = TFTrainer(\n\u001b[0m\u001b[1;32m     35\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining_args\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: TFTrainer.__init__() got an unexpected keyword argument 'data_collator'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "490fU5f--tvu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "T3tHJIWh-t_P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import accelerate"
      ],
      "metadata": {
        "id": "GC6hQhO2_IgY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install accelerate>=0.20.1"
      ],
      "metadata": {
        "id": "QLt74QXi-6mX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "UODQ-7YJ-uCK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import GPT2LMHeadModel, GPT2Tokenizer, GPT2Config\n",
        "from transformers import TextDataset, DataCollatorForLanguageModeling\n",
        "from transformers import Trainer, TrainingArguments\n",
        "\n",
        "# Load pre-trained GPT-2 model and tokenizer\n",
        "model = GPT2LMHeadModel.from_pretrained('gpt2')\n",
        "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
        "\n",
        "# Load your domain-specific data\n",
        "train_dataset = TextDataset(\n",
        "    tokenizer=tokenizer,\n",
        "     file_path='/content/advertising.csv',\n",
        "    block_size=128\n",
        ")\n",
        "\n",
        "# Define data collator\n",
        "data_collator = DataCollatorForLanguageModeling(\n",
        "    tokenizer=tokenizer,\n",
        "    mlm=False\n",
        ")\n",
        "\n",
        "# Define training arguments\n",
        "training_args = TrainingArguments(\n",
        "    output_dir='output_directory',\n",
        "    overwrite_output_dir=True,\n",
        "    num_train_epochs=3,\n",
        "    per_device_train_batch_size=8,\n",
        "    save_steps=10_000,\n",
        "    save_total_limit=2,\n",
        ")\n",
        "\n",
        "# Initialize Trainer\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    data_collator=data_collator,\n",
        "    train_dataset=train_dataset,\n",
        ")\n",
        "\n",
        "# Fine-tune the model\n",
        "trainer.train()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "id": "RhA-zKsS-vgU",
        "outputId": "7e1277c8-2048-4cdc-baad-ecdfefabfcdc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/data/datasets/language_modeling.py:53: FutureWarning: This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets library. You can have a look at this example script for pointers: https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ImportError",
          "evalue": "Using the `Trainer` with `PyTorch` requires `accelerate>=0.20.1`: Please run `pip install transformers[torch]` or `pip install accelerate -U`",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-84-a043037d0777>\u001b[0m in \u001b[0;36m<cell line: 23>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;31m# Define training arguments\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m training_args = TrainingArguments(\n\u001b[0m\u001b[1;32m     24\u001b[0m     \u001b[0moutput_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'output_directory'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m     \u001b[0moverwrite_output_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/training_args.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, output_dir, overwrite_output_dir, do_train, do_eval, do_predict, evaluation_strategy, prediction_loss_only, per_device_train_batch_size, per_device_eval_batch_size, per_gpu_train_batch_size, per_gpu_eval_batch_size, gradient_accumulation_steps, eval_accumulation_steps, eval_delay, learning_rate, weight_decay, adam_beta1, adam_beta2, adam_epsilon, max_grad_norm, num_train_epochs, max_steps, lr_scheduler_type, warmup_ratio, warmup_steps, log_level, log_level_replica, log_on_each_node, logging_dir, logging_strategy, logging_first_step, logging_steps, logging_nan_inf_filter, save_strategy, save_steps, save_total_limit, save_safetensors, save_on_each_node, no_cuda, use_cpu, use_mps_device, seed, data_seed, jit_mode_eval, use_ipex, bf16, fp16, fp16_opt_level, half_precision_backend, bf16_full_eval, fp16_full_eval, tf32, local_rank, ddp_backend, tpu_num_cores, tpu_metrics_debug, debug, dataloader_drop_last, eval_steps, dataloader_num_workers, past_index, run_name, disable_tqdm, remove_unused_columns, label_names, load_best_model_at_end, metric_for_best_model, greater_is_better, ignore_data_skip, fsdp, fsdp_min_num_params, fsdp_config, fsdp_transformer_layer_cls_to_wrap, deepspeed, label_smoothing_factor, optim, optim_args, adafactor, group_by_length, length_column_name, report_to, ddp_find_unused_parameters, ddp_bucket_cap_mb, ddp_broadcast_buffers, dataloader_pin_memo...\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/training_args.py\u001b[0m in \u001b[0;36m__post_init__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1440\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"pt\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mis_torch_available\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1442\u001b[0;31m             \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"cuda\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1443\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"npu\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1444\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"xpu\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/training_args.py\u001b[0m in \u001b[0;36mdevice\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1885\u001b[0m         \"\"\"\n\u001b[1;32m   1886\u001b[0m         \u001b[0mrequires_backends\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"torch\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1887\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_devices\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1888\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1889\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/utils/generic.py\u001b[0m in \u001b[0;36m__get__\u001b[0;34m(self, obj, objtype)\u001b[0m\n\u001b[1;32m     52\u001b[0m         \u001b[0mcached\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcached\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m             \u001b[0mcached\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     55\u001b[0m             \u001b[0msetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcached\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcached\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/training_args.py\u001b[0m in \u001b[0;36m_setup_devices\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1785\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_sagemaker_mp_enabled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1786\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_accelerate_available\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin_version\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"0.20.1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1787\u001b[0;31m                 raise ImportError(\n\u001b[0m\u001b[1;32m   1788\u001b[0m                     \u001b[0;34m\"Using the `Trainer` with `PyTorch` requires `accelerate>=0.20.1`: Please run `pip install transformers[torch]` or `pip install accelerate -U`\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1789\u001b[0m                 )\n",
            "\u001b[0;31mImportError\u001b[0m: Using the `Trainer` with `PyTorch` requires `accelerate>=0.20.1`: Please run `pip install transformers[torch]` or `pip install accelerate -U`",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "S9KUY1p2_rCe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AmL2lsAh_rMp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Fine-Tune using Keras"
      ],
      "metadata": {
        "id": "LHyCJot1_ywD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from transformers import GPT2Tokenizer, TFGPT2LMHeadModel\n",
        "\n",
        "# Load pre-trained GPT-2 model and tokenizer\n",
        "model = TFGPT2LMHeadModel.from_pretrained('gpt2')\n",
        "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
        "\n",
        "# Load your domain-specific data\n",
        "train_data_path = '/content/advertising.csv'\n",
        "with open(train_data_path, 'r', encoding='utf-8') as file:\n",
        "    train_texts = file.readlines()\n",
        "\n",
        "# Tokenize and encode the texts\n",
        "input_ids = tokenizer(train_texts, return_tensors='tf', padding=True, truncation=True)\n",
        "\n",
        "# Create TensorFlow Dataset\n",
        "train_dataset = tf.data.Dataset.from_tensor_slices((dict(input_ids), dict(input_ids)))\n",
        "\n",
        "# Define training parameters\n",
        "batch_size = 8\n",
        "num_epochs = 3\n",
        "learning_rate = 5e-5\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate),\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True))\n",
        "\n",
        "# Train the model\n",
        "model.fit(train_dataset.shuffle(1000).batch(batch_size),\n",
        "          epochs=num_epochs)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "R0z0ooQq_00x",
        "outputId": "c4f21cc1-81a1-48a3-c67e-4cef61538b24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All PyTorch model weights were used when initializing TFGPT2LMHeadModel.\n",
            "\n",
            "All the weights of TFGPT2LMHeadModel were initialized from the PyTorch model.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFGPT2LMHeadModel for predictions without further training.\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Asking to pad but the tokenizer does not have a padding token. Please select a token to use as `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` or add a new pad token via `tokenizer.add_special_tokens({'pad_token': '[PAD]'})`.",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-85-c2d81aa331df>\u001b[0m in \u001b[0;36m<cell line: 14>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;31m# Tokenize and encode the texts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0minput_ids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_texts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_tensors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'tf'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtruncation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;31m# Create TensorFlow Dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, text, text_pair, text_target, text_pair_target, add_special_tokens, padding, truncation, max_length, stride, is_split_into_words, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose, **kwargs)\u001b[0m\n\u001b[1;32m   2796\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_in_target_context_manager\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2797\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_switch_to_input_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2798\u001b[0;31m             \u001b[0mencodings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_one\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtext_pair\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtext_pair\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mall_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2799\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtext_target\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2800\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_switch_to_target_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py\u001b[0m in \u001b[0;36m_call_one\u001b[0;34m(self, text, text_pair, add_special_tokens, padding, truncation, max_length, stride, is_split_into_words, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose, **kwargs)\u001b[0m\n\u001b[1;32m   2882\u001b[0m                 )\n\u001b[1;32m   2883\u001b[0m             \u001b[0mbatch_text_or_text_pairs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtext_pair\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtext_pair\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2884\u001b[0;31m             return self.batch_encode_plus(\n\u001b[0m\u001b[1;32m   2885\u001b[0m                 \u001b[0mbatch_text_or_text_pairs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_text_or_text_pairs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2886\u001b[0m                 \u001b[0madd_special_tokens\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0madd_special_tokens\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py\u001b[0m in \u001b[0;36mbatch_encode_plus\u001b[0;34m(self, batch_text_or_text_pairs, add_special_tokens, padding, truncation, max_length, stride, is_split_into_words, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose, **kwargs)\u001b[0m\n\u001b[1;32m   3064\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3065\u001b[0m         \u001b[0;31m# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3066\u001b[0;31m         padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(\n\u001b[0m\u001b[1;32m   3067\u001b[0m             \u001b[0mpadding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpadding\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3068\u001b[0m             \u001b[0mtruncation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtruncation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py\u001b[0m in \u001b[0;36m_get_padding_truncation_strategies\u001b[0;34m(self, padding, truncation, max_length, pad_to_multiple_of, verbose, **kwargs)\u001b[0m\n\u001b[1;32m   2701\u001b[0m         \u001b[0;31m# Test if we have a padding token\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2702\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpadding_strategy\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mPaddingStrategy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDO_NOT_PAD\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpad_token\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpad_token_id\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2703\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m   2704\u001b[0m                 \u001b[0;34m\"Asking to pad but the tokenizer does not have a padding token. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2705\u001b[0m                 \u001b[0;34m\"Please select a token to use as `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Asking to pad but the tokenizer does not have a padding token. Please select a token to use as `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` or add a new pad token via `tokenizer.add_special_tokens({'pad_token': '[PAD]'})`."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ox86OOWDAPiN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from transformers import GPT2Tokenizer, TFGPT2LMHeadModel\n",
        "\n",
        "# Load pre-trained GPT-2 model and tokenizer\n",
        "model = TFGPT2LMHeadModel.from_pretrained('gpt2')\n",
        "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
        "\n",
        "# Add the EOS token as the pad token\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "# Load your domain-specific data\n",
        "train_data_path = '/content/advertising.csv'\n",
        "with open(train_data_path, 'r', encoding='utf-8') as file:\n",
        "    train_texts = file.readlines()\n",
        "\n",
        "# Tokenize and encode the texts\n",
        "input_ids = tokenizer(train_texts, return_tensors='tf', padding=True, truncation=True)\n",
        "\n",
        "# Create TensorFlow Dataset\n",
        "train_dataset = tf.data.Dataset.from_tensor_slices((dict(input_ids), dict(input_ids)))\n",
        "\n",
        "# Define training parameters\n",
        "batch_size = 8\n",
        "num_epochs = 3\n",
        "learning_rate = 5e-5\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate),\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True))\n",
        "\n",
        "# Train the model\n",
        "model.fit(train_dataset.shuffle(1000).batch(batch_size),\n",
        "          epochs=num_epochs)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 863
        },
        "id": "x8D8xj5OAPlT",
        "outputId": "39460ed2-4bbd-48e9-8aa0-73c26fbacff4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All PyTorch model weights were used when initializing TFGPT2LMHeadModel.\n",
            "\n",
            "All the weights of TFGPT2LMHeadModel were initialized from the PyTorch model.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFGPT2LMHeadModel for predictions without further training.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "in user code:\n\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 1401, in train_function  *\n        return step_function(self, iterator)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 1384, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 1373, in run_step  **\n        outputs = model.train_step(data)\n    File \"/usr/local/lib/python3.10/dist-packages/transformers/modeling_tf_utils.py\", line 1677, in train_step\n        self.optimizer.minimize(loss, self.trainable_variables, tape=tape)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/optimizers/optimizer.py\", line 543, in minimize\n        grads_and_vars = self.compute_gradients(loss, var_list, tape)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/optimizers/optimizer.py\", line 276, in compute_gradients\n        grads = tape.gradient(loss, var_list)\n\n    TypeError: Argument `target` should be a list or nested structure of Tensors, Variables or CompositeTensors to be differentiated, but received None.\n",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-86-5cdf87eae95c>\u001b[0m in \u001b[0;36m<cell line: 32>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;31m# Train the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m model.fit(train_dataset.shuffle(1000).batch(batch_size),\n\u001b[0m\u001b[1;32m     33\u001b[0m           epochs=num_epochs)\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\u001b[0m in \u001b[0;36mtf__train_function\u001b[0;34m(iterator)\u001b[0m\n\u001b[1;32m     13\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m                     \u001b[0mdo_return\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m                     \u001b[0mretval_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverted_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep_function\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfscope\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m                 \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m                     \u001b[0mdo_return\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/modeling_tf_utils.py\u001b[0m in \u001b[0;36mtrain_step\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m   1675\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1676\u001b[0m         \u001b[0;31m# Run backwards pass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1677\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainable_variables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1678\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1679\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompiled_metrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_state\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: in user code:\n\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 1401, in train_function  *\n        return step_function(self, iterator)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 1384, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 1373, in run_step  **\n        outputs = model.train_step(data)\n    File \"/usr/local/lib/python3.10/dist-packages/transformers/modeling_tf_utils.py\", line 1677, in train_step\n        self.optimizer.minimize(loss, self.trainable_variables, tape=tape)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/optimizers/optimizer.py\", line 543, in minimize\n        grads_and_vars = self.compute_gradients(loss, var_list, tape)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/optimizers/optimizer.py\", line 276, in compute_gradients\n        grads = tape.gradient(loss, var_list)\n\n    TypeError: Argument `target` should be a list or nested structure of Tensors, Variables or CompositeTensors to be differentiated, but received None.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gv2JMXOWAlRE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from transformers import GPT2Tokenizer, TFGPT2LMHeadModel\n",
        "\n",
        "# Load pre-trained GPT-2 model and tokenizer\n",
        "model = TFGPT2LMHeadModel.from_pretrained('gpt2')\n",
        "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
        "\n",
        "# Add the EOS token as the pad token\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "# Load your domain-specific data\n",
        "train_data_path = '/content/advertising.csv'\n",
        "with open(train_data_path, 'r', encoding='utf-8') as file:\n",
        "    train_texts = file.readlines()\n",
        "\n",
        "# Tokenize and encode the texts\n",
        "input_ids = tokenizer(train_texts, return_tensors='tf', padding=True, truncation=True)\n",
        "\n",
        "# Shift the input sequence to create target sequence\n",
        "labels = tf.roll(input_ids['input_ids'], shift=-1, axis=-1)\n",
        "\n",
        "# Create TensorFlow Dataset\n",
        "train_dataset = tf.data.Dataset.from_tensor_slices((dict(input_ids), labels))\n",
        "\n",
        "# Define training parameters\n",
        "batch_size = 8\n",
        "num_epochs = 3\n",
        "learning_rate = 5e-5\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate),\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction=tf.keras.losses.Reduction.NONE),\n",
        "              metrics=[tf.keras.metrics.SparseCategoricalAccuracy(name=\"accuracy\")])\n",
        "\n",
        "# Train the model\n",
        "model.fit(train_dataset.shuffle(1000).batch(batch_size),\n",
        "          epochs=num_epochs)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hFqo75c3Alaj",
        "outputId": "a423082e-389a-40a3-e0b7-e9414711555a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All PyTorch model weights were used when initializing TFGPT2LMHeadModel.\n",
            "\n",
            "All the weights of TFGPT2LMHeadModel were initialized from the PyTorch model.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFGPT2LMHeadModel for predictions without further training.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "126/126 [==============================] - 60s 206ms/step - loss: 2.2341 - accuracy: 0.5788\n",
            "Epoch 2/3\n",
            "126/126 [==============================] - 26s 207ms/step - loss: 1.6445 - accuracy: 0.6345\n",
            "Epoch 3/3\n",
            "126/126 [==============================] - 25s 202ms/step - loss: 1.5473 - accuracy: 0.6497\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7b3bc1f3ee60>"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    }
  ]
}