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  "cells": [
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      "cell_type": "markdown",
      "source": [
        "**Case Study 1: Generative Text for Customer Support Automation**\n",
        "\n",
        "Project Overview: Develop an AI-powered system to automate customer support interactions using generative models like GPT-3.5.\n",
        "\n",
        "Use Cases:\n",
        "1. Automated Response Generation:\n",
        "*   Problem Statement: Customer support teams are overwhelmed by repetitive inquiries that could be handled by automated systems\n",
        "*   Solution: Implement a generative AI model to automatically generate accurate and context-aware responses to common customer queries, reducing the load on human agents and improving response times.\n",
        "\n",
        "2. Personalized Customer Engagement:\n",
        "*   Problem Statement: Customers expect personalized interactions that cater to their specific needs and preferences.\n",
        "*   Solution: Use generative AI to create personalized engagement messages based on customer data and interaction history, enhancing customer satisfaction and loyalty.\n",
        "\n",
        "\n"
      ],
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          "text": [
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            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12\n",
            "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.82 nvidia-nvtx-cu12-12.1.105\n"
          ]
        }
      ],
      "source": [
        "pip install transformers torch pandas\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Create a sample customer support data\n",
        "data = {\n",
        "    'text': [\n",
        "        \"How can I reset my password?\",\n",
        "        \"What are your opening hours?\",\n",
        "        \"Can I change my subscription plan?\",\n",
        "        \"How do I update my billing information?\",\n",
        "        \"I have a problem with my order. Can you help?\",\n",
        "        \"Where can I find the user manual?\",\n",
        "        \"My account is locked. What should I do?\",\n",
        "        \"How do I contact customer support?\",\n",
        "        \"What is the refund policy?\",\n",
        "        \"Can I track my shipment online?\",\n",
        "        \"I need help with the installation process.\",\n",
        "        \"How can I cancel my order?\",\n",
        "        \"Is there a discount for bulk purchases?\",\n",
        "        \"Can I change the delivery address?\",\n",
        "        \"How do I use the promotional code?\",\n",
        "        \"The product I received is damaged. What now?\",\n",
        "        \"How long does the warranty last?\",\n",
        "        \"Can I return a product without the receipt?\",\n",
        "        \"How do I know if my payment was successful?\",\n",
        "        \"What payment methods do you accept?\"\n",
        "    ]\n",
        "}\n",
        "\n",
        "# Convert to DataFrame\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Save to CSV\n",
        "file_path = \"customer_support_data.csv\"\n",
        "df.to_csv(\"customer_support_data.csv\", index=False)\n"
      ],
      "metadata": {
        "id": "WiYSkamoCg5Z"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "#files.download(file_path)\n",
        "from google.colab import files\n",
        "uploaded = files.upload()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "id": "jWsWr9bEDAcQ",
        "outputId": "e41af163-7faa-472a-d198-43eb4b6fdb63"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-d29c10a4-ab87-452e-91a7-cf35b99aaa41\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-d29c10a4-ab87-452e-91a7-cf35b99aaa41\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving customer_support_data.csv to customer_support_data (1).csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import re\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "\n",
        "# Download NLTK resources\n",
        "nltk.download('punkt')\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RhMCoQRJDWzM",
        "outputId": "18791a27-53fa-4c38-d362-8ac6163d4ae6"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data cleaning and preprocessing function\n",
        "def preprocess_text(text):\n",
        "    # Convert text to lowercase\n",
        "    text = text.lower()\n",
        "    # Remove punctuation and numbers\n",
        "    text = re.sub(r'[^a-z\\s]', '', text)\n",
        "    # Tokenize text\n",
        "    words = word_tokenize(text)\n",
        "    # Remove stopwords\n",
        "    stop_words = set(stopwords.words('english'))\n",
        "    words = [word for word in words if word not in stop_words]\n",
        "    # Lemmatize words\n",
        "    lemmatizer = WordNetLemmatizer()\n",
        "    words = [lemmatizer.lemmatize(word) for word in words]\n",
        "    # Join words back into a single string\n",
        "    text = ' '.join(words)\n",
        "    return text\n",
        "\n",
        "# Apply preprocessing to the text data\n",
        "df['text'] = df['text'].apply(preprocess_text)\n",
        "\n",
        "# Save to CSV\n",
        "file_path = \"cleaned_customer_support_data.csv\"\n",
        "df.to_csv(file_path, index=False)"
      ],
      "metadata": {
        "id": "ULwAS_AVEVMK"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "files.download(file_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "THCYYF4nEcKc",
        "outputId": "595c8b5e-251a-4f92-9fc2-5319a9ca5f5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_ab3ac6b8-b5cb-4dfd-b98e-af5511b47eba\", \"cleaned_customer_support_data.csv\", 430)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from transformers import GPT2Tokenizer, GPT2LMHeadModel, Trainer, TrainingArguments\n",
        "from transformers import TextDataset, DataCollatorForLanguageModeling\n",
        "\n",
        "# Load your customer support data\n",
        "data = pd.read_csv('customer_support_data.csv')  # Ensure your CSV has a column 'text'\n",
        "\n",
        "# Save the text data to a file\n",
        "with open(\"train.txt\", \"w\") as f:\n",
        "    for line in data['text']:\n",
        "        f.write(line + \"\\n\")"
      ],
      "metadata": {
        "id": "JOvUb-JFEf8k"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to load dataset\n",
        "def load_dataset(file_path, tokenizer, block_size=128):\n",
        "    dataset = TextDataset(\n",
        "        tokenizer=tokenizer,\n",
        "        file_path=file_path,\n",
        "        block_size=block_size\n",
        "    )\n",
        "    return dataset"
      ],
      "metadata": {
        "id": "nAKAlPc0EtfP"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load pre-trained GPT-2 model and tokenizer\n",
        "model_name = \"gpt2\"\n",
        "tokenizer = GPT2Tokenizer.from_pretrained(model_name)\n",
        "model = GPT2LMHeadModel.from_pretrained(model_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400,
          "referenced_widgets": [
            "de2d2a20066c4a92928b55eca65371e0",
            "a7e120442be74c109579203b37296853",
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            "02b14e8a8229458fb67fb7195a5c994e",
            "3b0548f5a84c488d9b32f8d9d25903ad",
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            "c476dd913e254ac1b802c60e5fbfee80",
            "1bd21b56a06b441ba936496fa433ee4c",
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        },
        "id": "sEz6HVr4E11d",
        "outputId": "fa60a883-6b7a-405e-fcfd-3832b9762e52"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "de2d2a20066c4a92928b55eca65371e0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7a5c459909964d90a65c2f91f30700a8"
            }
          },
          "metadata": {}
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ed52039e01ec4095b5ae354331b18df0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0206feee7e484b10bdf1bf2b3afbd52e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "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": "860edb2c929343bd9a5efb65ad1d41c3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0cf27b9eb4804dd49b87c64d560a3965"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b2529dbce05e4b868db7ff13e480823d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare the dataset and data collator\n",
        "train_dataset = load_dataset(\"train.txt\", tokenizer)\n",
        "data_collator = DataCollatorForLanguageModeling(\n",
        "    tokenizer=tokenizer,\n",
        "    mlm=False,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h8y7ji7ZE5zu",
        "outputId": "e4d69579-a25e-49a3-914c-3cf74aeba0f8"
      },
      "execution_count": 9,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install accelerate -U"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AWapAc1VFCnn",
        "outputId": "6fc37424-d818-480a-9a81-4bb9bf8864a7"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting accelerate\n",
            "  Downloading accelerate-0.32.1-py3-none-any.whl (314 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m314.1/314.1 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy<2.0.0,>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (24.1)\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) (2.3.0+cu121)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.23.4)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.4.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.15.4)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.13.0)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.4)\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: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (8.9.2.26)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.3.1)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (11.0.2.54)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (10.3.2.106)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (11.4.5.107)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.0.106)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.20.5)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
            "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.3.0)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.10.0->accelerate) (12.5.82)\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) (4.66.4)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.5)\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) (3.7)\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) (2024.7.4)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Installing collected packages: accelerate\n",
            "Successfully installed accelerate-0.32.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers[torch]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9XuMVVMJFz4O",
        "outputId": "a7a6f856-4dfd-4c3b-8717-0004cf4ec49a"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers[torch] in /usr/local/lib/python3.10/dist-packages (4.41.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (3.15.4)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.23.4)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (24.1)\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]) (2024.5.15)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.19.1)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.4.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (4.66.4)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (2.3.0+cu121)\n",
            "Requirement already satisfied: accelerate>=0.21.0 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.32.1)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.21.0->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,>=0.23.0->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,>=0.23.0->transformers[torch]) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (1.13.0)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (3.1.4)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (8.9.2.26)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (12.1.3.1)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (11.0.2.54)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (10.3.2.106)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (11.4.5.107)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (12.1.0.106)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (2.20.5)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (12.1.105)\n",
            "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (2.3.0)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch->transformers[torch]) (12.5.82)\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]) (3.7)\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]) (2024.7.4)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->transformers[torch]) (2.1.5)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->transformers[torch]) (1.3.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip uninstall -y accelerate transformers\n",
        "!pip install accelerate transformers[torch] --quiet\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6BD-0NBNF0jA",
        "outputId": "33839f61-9f06-403c-dba4-9a8cd40cfc5a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found existing installation: accelerate 0.32.1\n",
            "Uninstalling accelerate-0.32.1:\n",
            "  Successfully uninstalled accelerate-0.32.1\n",
            "Found existing installation: transformers 4.41.2\n",
            "Uninstalling transformers-4.41.2:\n",
            "  Successfully uninstalled transformers-4.41.2\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.3/9.3 MB\u001b[0m \u001b[31m30.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import accelerate\n",
        "import transformers\n",
        "import torch\n",
        "\n",
        "print(\"Accelerate version:\", accelerate.__version__)\n",
        "print(\"Transformers version:\", transformers.__version__)\n",
        "print(\"Torch version:\", torch.__version__)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ybn5PwhGJZI",
        "outputId": "6effbd4c-ffd8-4201-a46e-9359c3998878"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accelerate version: 0.32.1\n",
            "Transformers version: 4.41.2\n",
            "Torch version: 2.3.0+cu121\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from packaging import version\n",
        "import accelerate\n",
        "\n",
        "required_version = \"0.21.0\"\n",
        "installed_version = accelerate.__version__\n",
        "\n",
        "if version.parse(installed_version) >= version.parse(required_version):\n",
        "    print(f\"Accelerate version {installed_version} is compatible.\")\n",
        "else:\n",
        "    raise ImportError(\n",
        "        f\"Accelerate version {installed_version} is not compatible. \"\n",
        "        f\"Please install accelerate>={required_version}.\"\n",
        "    )\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s7QwP8XtIRnx",
        "outputId": "8d688281-fb91-4db3-d021-455f761585e1"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accelerate version 0.32.1 is compatible.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers torch --quiet\n"
      ],
      "metadata": {
        "id": "wDViJ12mIYb9"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Tokenize the dataset\n",
        "def tokenize_function(examples):\n",
        "    return tokenizer(examples[\"text\"], truncation=True, padding=\"max_length\", max_length=512)"
      ],
      "metadata": {
        "id": "4el0Jo3uI1EI"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pyarrow==10.0.1 datasets==2.11.0 --quiet\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d_AVZH0uKMBG",
        "outputId": "b52b439e-7b0c-4a58-911f-a3f3d8996fc0"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m35.9/35.9 MB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m468.7/468.7 kB\u001b[0m \u001b[31m19.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 kB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m15.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.3/134.3 kB\u001b[0m \u001b[31m10.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 10.0.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip uninstall -y pyarrow datasets cudf-cu12\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ci8a1iE0RZXd",
        "outputId": "83b86f57-aace-45da-fd93-871083b75ba2"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found existing installation: pyarrow 10.0.1\n",
            "Uninstalling pyarrow-10.0.1:\n",
            "  Successfully uninstalled pyarrow-10.0.1\n",
            "Found existing installation: datasets 2.11.0\n",
            "Uninstalling datasets-2.11.0:\n",
            "  Successfully uninstalled datasets-2.11.0\n",
            "Found existing installation: cudf-cu12 24.4.1\n",
            "Uninstalling cudf-cu12-24.4.1:\n",
            "  Successfully uninstalled cudf-cu12-24.4.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pyarrow==14.0.1 datasets --quiet\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rRwIZJEqR_4p",
        "outputId": "6c6187ea-97b6-45e2-8cdf-ba8659001f33"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m38.0/38.0 MB\u001b[0m \u001b[31m12.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m547.8/547.8 kB\u001b[0m \u001b[31m30.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.1/542.1 kB\u001b[0m \u001b[31m36.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.9/64.9 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize Accelerator\n",
        "from accelerate import Accelerator\n",
        "accelerator = Accelerator()\n",
        "\n",
        "# Assuming you are using a huggingface transformer model\n",
        "from torch.optim import AdamW\n",
        "learning_rate = 1e-5 # Set your desired learning rate here\n",
        "optimizer = AdamW(model.parameters(), lr=learning_rate)\n",
        "\n",
        "# Prepare the model, optimizer, and dataloader for distributed training\n",
        "model, optimizer, train_dataloader = accelerator.prepare(\n",
        "    model,\n",
        "    optimizer,\n",
        "    train_dataset\n",
        ")"
      ],
      "metadata": {
        "id": "6nrwtBy6WJMY"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training loop\n",
        "#num_train_epochs = 1\n",
        "#for epoch in range(num_train_epochs):\n",
        "#    model.train()\n",
        " #   for step, batch in enumerate(train_dataloader):\n",
        "       # Assuming your model expects 'input_ids' and 'attention_mask'\n",
        "     # Modify these keys according to your model's requirements\n",
        "      #  outputs = model(input_ids=batch['input_ids'], attention_mask=batch['attention_mask'])\n",
        "       # loss = outputs.loss\n",
        "        #accelerator.backward(loss)\n",
        "        #optimizer.step()\n",
        "        #optimizer.zero_grad()\n",
        "        #if step % 100 == 0:\n",
        "         #   print(f\"Epoch {epoch}, Step {step}, Loss: {loss.item()}\")\n",
        "\n",
        "         # Training loop\n",
        "#num_train_epochs = 1\n",
        "#for epoch in range(num_train_epochs):\n",
        " #   model.train()\n",
        "  #  for step, batch in enumerate(train_dataloader):\n",
        "   #     # Assuming your model expects 'input_ids' and 'attention_mask'\n",
        "    #    # Modify these keys according to your model's requirements\n",
        "     #   # Check the shape of the batch and add a dimension if needed\n",
        "      #  if batch['input_ids'].dim() == 1:\n",
        "       #     batch['input_ids'] = batch['input_ids'].unsqueeze(0)\n",
        "        #if batch['attention_mask'].dim() == 1:\n",
        "         #   batch['attention_mask'] = batch['attention_mask'].unsqueeze(0)\n",
        "    #    outputs = model(input_ids=batch['input_ids'], attention_mask=batch['attention_mask'])\n",
        "     #   loss = outputs.loss\n",
        "      #  accelerator.backward(loss)\n",
        "       # optimizer.step()\n",
        "     #   optimizer.zero_grad()\n",
        "      #  if step % 100 == 0:\n",
        "       #     print(f\"Epoch {epoch}, Step {step}, Loss: {loss.item()}\")\n",
        "\n",
        "       # Training loop\n",
        "\n",
        "# Training loop\n",
        "num_train_epochs = 1\n",
        "for epoch in range(num_train_epochs):\n",
        "    model.train()\n",
        "    for step, batch in enumerate(train_dataloader):\n",
        "        # Assuming your model expects 'input_ids' and 'attention_mask'\n",
        "        # Modify these keys according to your model's requirements\n",
        "        # Check if batch elements are tensors before checking dimensions\n",
        "        if isinstance(batch['input_ids'], torch.Tensor) and batch['input_ids'].dim() == 1:\n",
        "            batch['input_ids'] = batch['input_ids'].unsqueeze(0)\n",
        "        if isinstance(batch['attention_mask'], torch.Tensor) and batch['attention_mask'].dim() == 1:\n",
        "            batch['attention_mask'] = batch['attention_mask'].unsqueeze(0)\n",
        "        outputs = model(input_ids=batch['input_ids'], attention_mask=batch['attention_mask'])\n",
        "        loss = outputs.loss\n",
        "        accelerator.backward(loss)\n",
        "        optimizer.step()\n",
        "        optimizer.zero_grad()\n",
        "        if step % 100 == 0:\n",
        "            print(f\"Epoch {epoch}, Step {step}, Loss: {loss.item()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "id": "0AM0arknrggN",
        "outputId": "0fdb8bdd-3e87-4395-be4b-ebb6571be121"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "too many indices for tensor of dimension 1",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-31-6ad6996d36c1>\u001b[0m in \u001b[0;36m<cell line: 40>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     44\u001b[0m         \u001b[0;31m# Modify these keys according to your model's requirements\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0;31m# Check if batch elements are tensors before checking dimensions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'input_ids'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'input_ids'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\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     47\u001b[0m             \u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'input_ids'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'input_ids'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\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[0m\n\u001b[1;32m     48\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'attention_mask'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'attention_mask'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndexError\u001b[0m: too many indices for tensor of dimension 1"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for epoch in range(num_train_epochs):\n",
        "    model.train()\n",
        "    for step, batch in enumerate(train_dataloader):\n",
        "        # Check if 'batch' is a dictionary before proceeding\n",
        "        if isinstance(batch, dict):\n",
        "            print(f\"Step {step} batch keys: {batch.keys()}\")  # Print keys of the batch dictionary\n",
        "            for key in batch:\n",
        "                # Check if the item is a tensor before printing its shape\n",
        "                if isinstance(batch[key], torch.Tensor):\n",
        "                    print(f\"{key} shape: {batch[key].shape}\")\n",
        "                else:\n",
        "                    print(f\"{key} is not a tensor\")\n",
        "        else:\n",
        "            print(f\"Step {step}: Batch is not a dictionary, it's a {type(batch)}\")\n",
        "        break  # Just inspect the first batch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wBIX3FD4u9AB",
        "outputId": "48279196-5d41-4442-d0c2-86e719c9386e"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Step 0: Batch is not a dictionary, it's a <class 'torch.Tensor'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "# Training loop\n",
        "num_train_epochs = 1\n",
        "for epoch in range(num_train_epochs):\n",
        "    model.train()\n",
        "    for step, batch in enumerate(train_dataloader):\n",
        "        # Print batch shape\n",
        "        print(f\"Step {step} batch shape: {batch.shape}\")\n",
        "\n",
        "        # Ensure tensors have the correct dimensions\n",
        "        if batch.dim() == 1:\n",
        "            batch = batch.unsqueeze(0)\n",
        "\n",
        "        # Move tensor to the appropriate device\n",
        "        batch = batch.to(accelerator.device)\n",
        "\n",
        "        # Assuming batch contains the input_ids and attention_mask\n",
        "        # If batch is a tensor, you may need to generate attention masks\n",
        "        attention_mask = torch.ones(batch.shape, device=batch.device)\n",
        "\n",
        "        # Forward pass\n",
        "        outputs = model(input_ids=batch, attention_mask=attention_mask, labels=batch) # Add labels for loss calculation\n",
        "        # Check if model outputs a loss directly\n",
        "        if hasattr(outputs, 'loss'):\n",
        "            loss = outputs.loss\n",
        "        else:\n",
        "            # Calculate loss manually if needed (example with cross-entropy loss)\n",
        "            loss_fn = torch.nn.CrossEntropyLoss()\n",
        "            logits = outputs.logits  # Assuming your model outputs logits\n",
        "            loss = loss_fn(logits.view(-1, logits.size(-1)), batch.view(-1))\n",
        "\n",
        "        # Backward pass and optimization\n",
        "        accelerator.backward(loss)\n",
        "        optimizer.step()\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        if step % 100 == 0:\n",
        "            print(f\"Epoch {epoch}, Step {step}, Loss: {loss.item()}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5rD3JUEJxTB7",
        "outputId": "2ada1549-6a6d-463e-a21f-685e798c5736"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Step 0 batch shape: torch.Size([128])\n",
            "Epoch 0, Step 0, Loss: 2.312190532684326\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the model\n",
        "model.save_pretrained(\"./gpt2-customer-support\")\n",
        "tokenizer.save_pretrained(\"./gpt2-customer-support\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zzF7G95RxsAm",
        "outputId": "1e50ae93-e446-4dd9-c90f-0641a144c576"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('./gpt2-customer-support/tokenizer_config.json',\n",
              " './gpt2-customer-support/special_tokens_map.json',\n",
              " './gpt2-customer-support/vocab.json',\n",
              " './gpt2-customer-support/merges.txt',\n",
              " './gpt2-customer-support/added_tokens.json')"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the fine-tuned model and tokenizer\n",
        "model = GPT2LMHeadModel.from_pretrained(\"./gpt2-customer-support\")\n",
        "tokenizer = GPT2Tokenizer.from_pretrained(\"./gpt2-customer-support\")"
      ],
      "metadata": {
        "id": "immdsLpkzcMN"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to generate responses\n",
        "def generate_response(prompt):\n",
        "    inputs = tokenizer.encode(prompt, return_tensors='pt')\n",
        "    outputs = model.generate(inputs, max_length=150, num_return_sequences=1)\n",
        "    response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
        "    return response"
      ],
      "metadata": {
        "id": "Kn2ymHxzzgNG"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_response(prompt):\n",
        "    # Tokenize the input prompt\n",
        "    inputs = tokenizer(prompt, return_tensors='pt')\n",
        "\n",
        "    # Generate attention mask\n",
        "    attention_mask = inputs['attention_mask']\n",
        "\n",
        "    # Generate response\n",
        "    outputs = model.generate(\n",
        "        input_ids=inputs['input_ids'],\n",
        "        attention_mask=attention_mask,\n",
        "        max_length=250,  # Increase the maximum length for a more detailed response\n",
        "        min_length=50,   # Ensure a minimum length for the response\n",
        "        num_return_sequences=1,\n",
        "        pad_token_id=tokenizer.eos_token_id,\n",
        "        no_repeat_ngram_size=3,  # Prevents repetition of 3-grams\n",
        "        num_beams=5,  # Beam search for better output\n",
        "        temperature=0.7,  # Control the randomness of predictions\n",
        "        top_k=50,  # Consider the top 50 tokens by probability\n",
        "        top_p=0.9  # Nucleus sampling - consider the top 90% of probability mass\n",
        "    )\n",
        "\n",
        "    # Decode the output\n",
        "    response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
        "\n",
        "    # Add specific instructions for changing the password\n",
        "    if \"change my password\" in prompt.lower() or \"reset my password\" in prompt.lower():\n",
        "        response += \"\\n\\nTo change your password, follow these steps:\\n\"\n",
        "        response += \"1. Log in to your account.\\n\"\n",
        "        response += \"2. Go to 'Account Settings' or 'Profile'.\\n\"\n",
        "        response += \"3. Click on 'Security' or 'Password Management'.\\n\"\n",
        "        response += \"4. Enter your current password.\\n\"\n",
        "        response += \"5. Enter your new password and confirm it.\\n\"\n",
        "        response += \"6. Save the changes.\\n\"\n",
        "        response += \"If you encounter any issues, please contact support for further assistance.\"\n",
        "\n",
        "    return response\n",
        "\n",
        "# Example usage\n",
        "customer_query = \"How can I reset my password?\"\n",
        "response = generate_response(customer_query)\n",
        "print(\"AI Response:\", response)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4cL3TnLgzi7r",
        "outputId": "3957e450-991d-46ca-c06e-b9f6157824e3"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AI Response: How can I reset my password?\n",
            "\n",
            "You can reset your password at any time by going to Settings > Security > Reset Password.\n",
            "\n",
            "How do I change my password if I don't want to use my account\n",
            "\n",
            "If you want to change your password, you can do so by following these steps:\n",
            "\n",
            "To change your password, follow these steps:\n",
            "1. Log in to your account.\n",
            "2. Go to 'Account Settings' or 'Profile'.\n",
            "3. Click on 'Security' or 'Password Management'.\n",
            "4. Enter your current password.\n",
            "5. Enter your new password and confirm it.\n",
            "6. Save the changes.\n",
            "If you encounter any issues, please contact support for further assistance.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_response(prompt):\n",
        "    # Tokenize the input prompt\n",
        "    inputs = tokenizer(prompt, return_tensors='pt')\n",
        "\n",
        "    # Generate attention mask\n",
        "    attention_mask = inputs['attention_mask']\n",
        "\n",
        "    # Generate response\n",
        "    outputs = model.generate(\n",
        "        input_ids=inputs['input_ids'],\n",
        "        attention_mask=attention_mask,\n",
        "        max_length=250,  # Increase the maximum length for a more detailed response\n",
        "        min_length=50,   # Ensure a minimum length for the response\n",
        "        num_return_sequences=1,\n",
        "        pad_token_id=tokenizer.eos_token_id,\n",
        "        no_repeat_ngram_size=3,  # Prevents repetition of 3-grams\n",
        "        num_beams=5,  # Beam search for better output\n",
        "        temperature=0.7,  # Control the randomness of predictions\n",
        "        top_k=50,  # Consider the top 50 tokens by probability\n",
        "        top_p=0.9  # Nucleus sampling - consider the top 90% of probability mass\n",
        "    )\n",
        "\n",
        "    # Decode the output\n",
        "    response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
        "    return response\n",
        "\n",
        "# Example usage\n",
        "customer_query = \"How can I cancel my order?\"\n",
        "response = generate_response(customer_query)\n",
        "print(\"AI Response:\", response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NfSX02JJzlGy",
        "outputId": "f8a49bb4-40a4-4497-c3e5-d476e3b813ac"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AI Response: How can I cancel my order?\n",
            "\n",
            "You can cancel your order at any time by clicking the button below.\n",
            "\n",
            "If you have any questions about your order, please call us at 1-800-744-7000 or email us at customerservice@crowdfunding.com. We will be happy to answer any questions you may have. Thank you!\n",
            "\n",
            "Thank you for your interest in Crowdfunding. We look forward to hearing from you soon!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IufbBzGkBnzy"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}