NDUWorkshopSolutions.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Let's Start by Installing Dependencies"
      ],
      "metadata": {
        "id": "VwZhW7iUdhqm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers accelerate bitsandbytes>=0.39.0  -qU"
      ],
      "metadata": {
        "id": "dcTEu-CZOo0m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create a text-generation pipeline with GPT-NEO open-source model"
      ],
      "metadata": {
        "id": "Cf4aJam-dyse"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "# put the name of the pipeline\n",
        "# put the name of the model. get it from HuggingFace\n",
        "# https://huggingface.co/EleutherAI/gpt-neo-125m\n",
        "generator = pipeline('text-generation', model ='EleutherAI/gpt-neo-125M')\n"
      ],
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        {
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          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.01k [00:00<?, ?B/s]"
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        {
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          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/526M [00:00<?, ?B/s]"
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        {
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          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/560 [00:00<?, ?B/s]"
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          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
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        {
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          "data": {
            "text/plain": [
              "Downloading (…)olve/main/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": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/357 [00:00<?, ?B/s]"
            ],
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Now that pipeline is called, let's test the text-generation feature!"
      ],
      "metadata": {
        "id": "GbyoNok_eJTo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "post = \"This is my first blog post, I'm really excited!\"\n",
        "result = generator(post, max_length=50, do_sample=True, temperature=0.9)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mdh2S7IJUgul",
        "outputId": "6f4d3165-6388-4205-9222-f6a4c8caf576"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aAwKYjt7Ur_z",
        "outputId": "31dcff9d-ea81-467f-8251-27a6ba551db5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'generated_text': \"This is my first blog post, I'm really excited! I've been writing this blog for a long time, and just started a new one for my new family and friends, so I'm taking this time off when I think about it!\\n\"}]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "post = \"Write a python function that takes two numbers as function params and returns their convolution equation. Star with code:\\n\"\n",
        "result = generator(post, max_length=512, do_sample=True, temperature=0.9)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZH8-PEJzUxO5",
        "outputId": "3b76122f-5d47-4ac7-d85b-4713ab5bd13a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result[0]['generated_text']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83
        },
        "id": "lu3Ylp-IU66G",
        "outputId": "9daafd64-11b4-4919-849b-a57ccaeaa26b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"Write a python function that takes two numbers as function params and returns their convolution equation. Star with code:\\n>>> myInt = [1,2]\\n>>> my = np.array(myVar(int) for int in range((4,'f','n'))))\\n>>> my = np.array(myVar(2))\\n>>> my\\narray([[1,                                                                         \\n              [2],                                                                      \\n              [3,                                                                     \\n               [4,                                                                     \\n               [5,                                                                      \\n           \""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Finetuning\n",
        "\n",
        "Finetuning is the process of feeding the model the specific data we need it to understand and answer based on it\n",
        "\n",
        "In order to finetune the model, we need to call it from the pretrained instance on Huggingface"
      ],
      "metadata": {
        "id": "LQTLCdykeOrq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import GPT2LMHeadModel, GPT2Tokenizer\n",
        "model_name = \"gpt2\" #we will use the gpt2 default model\n",
        "\n",
        "# we need a model and a tokenizer\n",
        "\n",
        "model = GPT2LMHeadModel.from_pretrained(model_name)\n",
        "tokenizer = GPT2Tokenizer.from_pretrained(model_name)\n"
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          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
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            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "842850dca1014a6c92bff5d0820b9aa4"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8786eab91da54b55970d5c00b7d612e8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9f8efb8ce1104256840199bb31ce7c05"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Time to check the model performance before finetuning"
      ],
      "metadata": {
        "id": "wsbTHessflZr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#now that we called the model\n",
        "# we need to generate text\n",
        "#remember we need to encode the input ( tokenize it )\n",
        "#and decode the output ( de-tokenize it , bring it back to text from tokenx )\n",
        "def generate_text(prompt, max_length=100, temperature=0.7):\n",
        "    input_ids = tokenizer.encode(prompt, return_tensors=\"pt\")\n",
        "    output_ids = model.generate(input_ids, max_length=max_length, temperature=temperature)\n",
        "    generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)\n",
        "    return generated_text\n"
      ],
      "metadata": {
        "id": "Pxaui8j6WR8Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "and here's an example:"
      ],
      "metadata": {
        "id": "YjIJhtt-fxQS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = \"In a futuristic world, \"\n",
        "generated_text = generate_text(prompt, max_length=150, temperature=0.8)\n",
        "print(generated_text)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yr10aeCFWah_",
        "outputId": "c303016e-e62b-491f-e0a4-90111689ef12"
      },
      "execution_count": null,
      "outputs": [
        {
          "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 `0.8` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\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": [
            "In a futuristic world,  the world is a place of chaos and chaos.  The world is a place of chaos and chaos.  The world is a place of chaos and chaos.  The world is a place of chaos and chaos.  The world is a place of chaos and chaos.  The world is a place of chaos and chaos.  The world is a place of chaos and chaos.  The world is a place of chaos and chaos.  The world is a place of chaos and chaos.  The world is a place of chaos and chaos.  The world is a place of chaos and chaos.  The world is a place of chaos and chaos. \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "whenever we want to finetune, we need to preprocess our data\n",
        "\n",
        "The data we are dealing with is from Kaggle\n",
        "https://www.kaggle.com/datasets/elvis23/mental-health-conversational-data/"
      ],
      "metadata": {
        "id": "AEaTacgXfyl_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "def preprocess_intents_json(intents_file):\n",
        "    with open(intents_file, \"r\") as f:\n",
        "        data = json.load(f)\n",
        "\n",
        "    preprocessed_data = []\n",
        "\n",
        "    for intent in data[\"intents\"]:\n",
        "        for pattern in intent[\"patterns\"]:\n",
        "            preprocessed_data.append(f\"User: {pattern}\\n\")\n",
        "            for response in intent[\"responses\"]:\n",
        "                preprocessed_data.append(f\"Assistant: {response}\\n\")\n",
        "\n",
        "    return \"\".join(preprocessed_data)\n",
        "\n",
        "def save_preprocessed_data(preprocessed_data, output_file):\n",
        "    with open(output_file, \"w\") as f:\n",
        "        f.write(preprocessed_data)\n",
        "\n"
      ],
      "metadata": {
        "id": "3Q1Wd8lOWcpZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Preprocess data:"
      ],
      "metadata": {
        "id": "JqxU_OCqggMM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "intents_file = \"intents.json\"\n",
        "output_file = \"mental_health_data.txt\"\n",
        "\n",
        "preprocessed_data = preprocess_intents_json(intents_file)\n",
        "save_preprocessed_data(preprocessed_data, output_file)"
      ],
      "metadata": {
        "id": "EIrh_hWqgho0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Time to finetune the model: for finetuning, as we mentioned before, we need to call the model separated: model and tokenizer"
      ],
      "metadata": {
        "id": "-TPVuJ13f-s8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#import required libraries\n",
        "from transformers import GPT2LMHeadModel, GPT2Tokenizer, GPT2Config\n",
        "from transformers import TextDataset, DataCollatorForLanguageModeling\n",
        "from transformers import Trainer, TrainingArguments\n",
        "\n"
      ],
      "metadata": {
        "id": "HVYSjT9vgsxJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In python, a clean code is a code that can speak itself\n",
        "\n",
        "in order to keep our code clean, divide each \"functional part\" to a separate funciton\n",
        "\n",
        "each part that does onething, put it in a separate function and call it\n",
        "Here's what we did for finetuning function"
      ],
      "metadata": {
        "id": "c0XcvBjEgvIm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "def fine_tune_gpt2(model_name, train_file, output_dir):\n",
        "    # Load GPT-2 model and tokenizer\n",
        "    model = GPT2LMHeadModel.from_pretrained(model_name)\n",
        "    tokenizer = GPT2Tokenizer.from_pretrained(model_name)\n",
        "\n",
        "# Load training dataset\n",
        "    train_dataset = TextDataset(\n",
        "        tokenizer=tokenizer,\n",
        "        file_path=train_file,\n",
        "        block_size=128)\n",
        "    # Create data collator for language modeling\n",
        "    data_collator = DataCollatorForLanguageModeling(\n",
        "        tokenizer=tokenizer, mlm=False)\n",
        "    # Set training arguments\n",
        "    training_args = TrainingArguments(\n",
        "        output_dir=output_dir,\n",
        "        overwrite_output_dir=True,\n",
        "        num_train_epochs=5,\n",
        "        per_device_train_batch_size=4,\n",
        "        save_steps=10_000,\n",
        "        save_total_limit=2,\n",
        "    )\n",
        "    # Train the model\n",
        "    trainer = Trainer(\n",
        "        model=model,\n",
        "        args=training_args,\n",
        "        data_collator=data_collator,\n",
        "        train_dataset=train_dataset,\n",
        "    )\n",
        "    trainer.train()\n",
        "    # Save the fine-tuned model\n",
        "    model.save_pretrained(output_dir)\n",
        "    tokenizer.save_pretrained(output_dir)\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "3uw6FeAGZB8J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Time to finetune!"
      ],
      "metadata": {
        "id": "zG8Ooy4Sg-Ty"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fine_tune_gpt2(\"gpt2\", \"mental_health_data.txt\", \"output\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "tDG-s9c1g-AO",
        "outputId": "26b7165d-ad11-4845-942d-ba959083638d"
      },
      "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": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='160' max='160' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [160/160 00:26, Epoch 5/5]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import PreTrainedTokenizerFast, GPT2LMHeadModel, GPT2TokenizerFast, GPT2Tokenizer\n",
        "\n",
        "\n",
        "\n",
        "def load_model(model_path):\n",
        "    model = GPT2LMHeadModel.from_pretrained(model_path)\n",
        "    return model\n",
        "\n",
        "\n",
        "def load_tokenizer(tokenizer_path):\n",
        "    tokenizer = GPT2Tokenizer.from_pretrained(tokenizer_path)\n",
        "    return tokenizer\n",
        "\n",
        "\n",
        "def generate_text(sequence, max_length):\n",
        "    model_path = \"output\"\n",
        "    model = load_model(model_path)\n",
        "    tokenizer = load_tokenizer(model_path)\n",
        "    ids = tokenizer.encode(f'{sequence}', return_tensors='pt')\n",
        "    final_outputs = model.generate(\n",
        "        ids,\n",
        "        do_sample=True,\n",
        "        max_length=max_length,\n",
        "        pad_token_id=model.config.eos_token_id,\n",
        "        top_k=50,\n",
        "        top_p=0.95,\n",
        "    )\n",
        "    print(tokenizer.decode(final_outputs[0], skip_special_tokens=True))\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "82R5hdObZE3a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# sequence = input() # oil price\n",
        "sequence = \"User: I feel overwhelmed today\"\n",
        "max_len = 30\n",
        "generate_text(sequence, max_len) # oil price for July June which had been low at as low as was originally stated Prices have since resumed"
      ],
      "metadata": {
        "id": "pYcI20VBiM7s"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}