Sakhi_Framework.ipynb

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  "cells": [
    {
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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Mk-lab-sudo/Sakhi-Phase1/blob/main/Sakhi_Framework.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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      "source": [
        "!pip install transformers\n",
        "!pip install rasa\n",
        "!pip install datasets\n"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "hVdHsbii3XiU",
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      },
      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.47.1)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n"
      ],
      "metadata": {
        "id": "MBEcbMof3gBG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the GPT-Neo model\n",
        "generator = pipeline(\"text-generation\", model=\"EleutherAI/gpt-neo-1.3B\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350,
          "referenced_widgets": [
            "05036ec36c524e6386765e07fb7fafb9",
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            "05735da829bf47b0b52695de2342cda6",
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            "4427d528a6264254b800079d34bd597a",
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            "4174407c55d444b59ba65f1c9fc7b793",
            "26e36840ffab4d038663c43392257c75",
            "e17023bab2dc4af7a3e6d66196a37cb0",
            "dcf92205304d4c69b690498f55adcc60",
            "a0fa723b0e5d42e4894bdc18bf6de754",
            "255c6613c75945978e0c2ef590c6b13b",
            "cb76f066566a45c9a61468a613a037f8",
            "a1f102df26634cdbb8155618c3e3a92c",
            "9486ff9bdd55473b9cab52f16d92981c",
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            "02c2534bcd74416dadbd68526983e6ac",
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            "62632f99819c419d8ab242a24df9c4d2",
            "8613b32a0f9143229bd41d186ae167e8",
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            "55305124e3df48c18244fc1bf7190a3d",
            "988ab430570c41998f1228a48df95a2a",
            "43c7412a3e944c938678499a51eb1ea2",
            "898ddf67f7a348fb8be86eb236cf39fb",
            "e54fa2909c1f41ee8968155a522e53ff",
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            "99952f7850c642068ce21ef2ce84061d"
          ]
        },
        "id": "pSPdIUG6G7fh",
        "outputId": "211865a6-4d59-42a1-d6ac-fecd69a593a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: 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": [
              "config.json:   0%|          | 0.00/1.35k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "05036ec36c524e6386765e07fb7fafb9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/5.31G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9c8dd33c240044549e4c3ad1245bfb90"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/200 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cf6845605642433ca3237d4b581406d2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/798k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4836d48fbca74b9b951c7c03d2426102"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b1e2b0125d4449fa8c836c0b35747470"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/90.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ef1a34d0a43a4abb9a681e36c9079845"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Device set to use cpu\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test Sakhi with a prompt\n",
        "prompt = \"Hello, I am Sakhi, your AI assistant. How can I assist you today?\"\n",
        "result = generator(prompt, max_length=50, num_return_sequences=1)\n",
        "print(result[0]['generated_text'])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sc2Ht1nRHFiu",
        "outputId": "00b744db-4e04-4f0f-9133-54dc1a57559b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hello, I am Sakhi, your AI assistant. How can I assist you today?\n",
            "\n",
            "A: Hello, AI!\n",
            "\n",
            "Q: How can I assist you today?\n",
            "\n",
            "A: You can try the following methods.\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "# Use an alternative conversational dataset\n",
        "dataset = load_dataset(\"daily_dialog\", split=\"train\")\n",
        "\n",
        "# Preview the first entry\n",
        "print(dataset[0])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316,
          "referenced_widgets": [
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            "916fc7ca684642898705b6a2fc99c675",
            "2a4265b86a664073aa741b5890ae29af",
            "91d405a86c0f41c7bbd8c0f1a443dcae",
            "dd7b8d50ae1a4fbb9f37b017d16a4ec0",
            "65e3133752e04f55a02e52714f1f1df5",
            "102535a6bdb84e9c9a1a81cd501b859b",
            "3a4eafcee2394fba8cb134f6339965ff",
            "37076d84b72b431ca7417a35070acdd1",
            "8ad27ad57258447a8cae2b2a1aa4bf17",
            "27f78125c6194488974904bc592dfec2",
            "9f69810de16e44efbd045a9b74e1066f",
            "16b2d1b181bf423984c8b77288ac1a70",
            "f9bbea9a31c44fa9b5be51b827251ab6",
            "b6752ac87bf948c88150cdac9675db1a",
            "5bb28fff829e4d23b80b8a72bea06c19"
          ]
        },
        "id": "7-1f_YEUHawN",
        "outputId": "bc8333dd-0d44-403e-a46e-825238ca2a96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/7.27k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3dde762208304bf58ca8a6b29747df10"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "daily_dialog.py:   0%|          | 0.00/4.85k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4ba1c9983ed24ca68dc6367755e8f249"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The repository for daily_dialog contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/daily_dialog.\n",
            "You can avoid this prompt in future by passing the argument `trust_remote_code=True`.\n",
            "\n",
            "Do you wish to run the custom code? [y/N] y\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/4.48M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "42e6e68539454cc0b254d82a042bf9a1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/11118 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "833948d361e547f0864d8cc15a03c566"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bacb05ac90e64fd6b7d4b25a8271c949"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "65e3133752e04f55a02e52714f1f1df5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'dialog': ['Say , Jim , how about going for a few beers after dinner ? ', ' You know that is tempting but is really not good for our fitness . ', ' What do you mean ? It will help us to relax . ', \" Do you really think so ? I don't . It will just make us fat and act silly . Remember last time ? \", \" I guess you are right.But what shall we do ? I don't feel like sitting at home . \", ' I suggest a walk over to the gym where we can play singsong and meet some of our friends . ', \" That's a good idea . I hear Mary and Sally often go there to play pingpong.Perhaps we can make a foursome with them . \", ' Sounds great to me ! If they are willing , we could ask them to go dancing with us.That is excellent exercise and fun , too . ', \" Good.Let ' s go now . \", ' All right . '], 'act': [3, 4, 2, 2, 2, 3, 4, 1, 3, 4], 'emotion': [0, 0, 0, 0, 0, 0, 4, 4, 4, 4]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the generator model\n",
        "generator.save_pretrained(\"sakhi_nlp_model\")\n"
      ],
      "metadata": {
        "id": "2ZTPjowBHwUB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "# Load the dataset (e.g., daily_dialog)\n",
        "dataset = load_dataset(\"daily_dialog\", split=\"train\")\n"
      ],
      "metadata": {
        "id": "oIrheDJ7IujG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def format_data(sample):\n",
        "    return {\n",
        "        \"prompt\": sample[\"dialog\"][0],\n",
        "        \"response\": sample[\"dialog\"][1] if len(sample[\"dialog\"]) > 1 else \"\"\n",
        "    }\n"
      ],
      "metadata": {
        "id": "hhtBVVz6KJhy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "formatted_data = dataset.map(format_data)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "b3929914ecaa4fe185710c73c0e9df7d",
            "fef44a024bd4411faf20596b2e3d3126",
            "b5f87fadbf584236a8ff84afc3686112",
            "000adc57eeda4a3999a822a645213015",
            "55793e8cc3f646f4a0c6f39ca0a263c9",
            "43087d65bde44390af4ffaa70859b3f3",
            "d5d8238f2b864748a2019e1bfd5b6998",
            "30a80ed4e8f74760963cad8e3003fe61",
            "6bf20f62f0d34fbf921546c3b470e224",
            "998ae4cbe1d94e118b328299ae7cb924",
            "2a1a4559ec3243e2a04c32325f12bfe6"
          ]
        },
        "id": "mrYVfy-BK_22",
        "outputId": "b0982117-1690-438b-cc3c-a2bbd598c780"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/11118 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b3929914ecaa4fe185710c73c0e9df7d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "# Convert to a list of dictionaries and save to a JSON file\n",
        "formatted_data_list = [item for item in formatted_data]\n",
        "with open(\"daily_dialog_formatted.json\", \"w\") as f:\n",
        "    json.dump(formatted_data_list, f, indent=4)\n"
      ],
      "metadata": {
        "id": "tXz7ytmdLD68"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(formatted_data))  # Should print: <class 'datasets.Dataset'>\n",
        "print(len(formatted_data))   # Number of entries\n",
        "print(formatted_data[0])     # Check the first formatted entry\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rtiDEqjXLm8m",
        "outputId": "5433abd5-9bdd-4531-bebb-06d86d24e631"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'datasets.arrow_dataset.Dataset'>\n",
            "11118\n",
            "{'dialog': ['Say , Jim , how about going for a few beers after dinner ? ', ' You know that is tempting but is really not good for our fitness . ', ' What do you mean ? It will help us to relax . ', \" Do you really think so ? I don't . It will just make us fat and act silly . Remember last time ? \", \" I guess you are right.But what shall we do ? I don't feel like sitting at home . \", ' I suggest a walk over to the gym where we can play singsong and meet some of our friends . ', \" That's a good idea . I hear Mary and Sally often go there to play pingpong.Perhaps we can make a foursome with them . \", ' Sounds great to me ! If they are willing , we could ask them to go dancing with us.That is excellent exercise and fun , too . ', \" Good.Let ' s go now . \", ' All right . '], 'act': [3, 4, 2, 2, 2, 3, 4, 1, 3, 4], 'emotion': [0, 0, 0, 0, 0, 0, 4, 4, 4, 4], 'prompt': 'Say , Jim , how about going for a few beers after dinner ? ', 'response': ' You know that is tempting but is really not good for our fitness . '}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the generator model\n",
        "generator.save_pretrained(\"sakhi_nlp_model\")\n"
      ],
      "metadata": {
        "id": "gmfbHX89Lshb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_prompts = [\n",
        "    \"What is your name?\",\n",
        "    \"Can you help me plan my day?\",\n",
        "    \"Tell me a joke!\",\n",
        "]\n",
        "\n",
        "for prompt in test_prompts:\n",
        "    result = generator(prompt, max_length=50, num_return_sequences=1)\n",
        "    print(f\"Prompt: {prompt}\")\n",
        "    print(f\"Response: {result[0]['generated_text']}\\n\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EbqjeOEqL4ii",
        "outputId": "fe01d3bc-6c94-417a-a5fb-8d348184dfec",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: What is your name?\n",
            "Response: What is your name?\n",
            "\n",
            "A:\n",
            "\n",
            "My name is \n",
            "\n",
            "Szabo Zsambok\n",
            "\n",
            "I also have the following two entries in dbo.tblSales where your Szabo is located:-\n",
            "s\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: Can you help me plan my day?\n",
            "Response: Can you help me plan my day?\n",
            "\n",
            "Is there someone I should know about?\n",
            "\n",
            "Do you think my life needs some form of attention?\n",
            "\n",
            "This is the question I come to most often, but I have also received it in\n",
            "\n",
            "Prompt: Tell me a joke!\n",
            "Response: Tell me a joke!\n",
            "\n",
            "I am a writer and I love writing. But even I couldn’t write an autobiography without a few funny lines. This is a story of my life as a writer, that I hope can be enjoyable for\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install wandb\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ESb9NUHiMPD6",
        "outputId": "9c1317e8-f68b-4837-d49f-1ba7d77843c4",
        "collapsed": true
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: wandb in /usr/local/lib/python3.10/dist-packages (0.19.1)\n",
            "Requirement already satisfied: click!=8.0.0,>=7.1 in /usr/local/lib/python3.10/dist-packages (from wandb) (8.1.7)\n",
            "Requirement already satisfied: docker-pycreds>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (0.4.0)\n",
            "Requirement already satisfied: gitpython!=3.1.29,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (3.1.43)\n",
            "Requirement already satisfied: platformdirs in /usr/local/lib/python3.10/dist-packages (from wandb) (4.3.6)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=5.28.0,<6,>=3.19.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (4.23.3)\n",
            "Requirement already satisfied: psutil>=5.0.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (5.9.5)\n",
            "Requirement already satisfied: pydantic<3,>=2.6 in /usr/local/lib/python3.10/dist-packages (from wandb) (2.10.4)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from wandb) (6.0.2)\n",
            "Requirement already satisfied: requests<3,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (2.32.3)\n",
            "Requirement already satisfied: sentry-sdk>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (2.19.2)\n",
            "Requirement already satisfied: setproctitle in /usr/local/lib/python3.10/dist-packages (from wandb) (1.3.4)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from wandb) (75.1.0)\n",
            "Requirement already satisfied: typing-extensions<5,>=4.4 in /usr/local/lib/python3.10/dist-packages (from wandb) (4.12.2)\n",
            "Requirement already satisfied: six>=1.4.0 in /usr/local/lib/python3.10/dist-packages (from docker-pycreds>=0.4.0->wandb) (1.17.0)\n",
            "Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.10/dist-packages (from gitpython!=3.1.29,>=1.0.0->wandb) (4.0.11)\n",
            "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=2.6->wandb) (0.7.0)\n",
            "Requirement already satisfied: pydantic-core==2.27.2 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=2.6->wandb) (2.27.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (2024.12.14)\n",
            "Requirement already satisfied: smmap<6,>=3.0.1 in /usr/local/lib/python3.10/dist-packages (from gitdb<5,>=4.0.1->gitpython!=3.1.29,>=1.0.0->wandb) (5.0.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from transformers import TrainingArguments, Trainer, AutoModelForCausalLM, AutoTokenizer, DataCollatorWithPadding\n",
        "from datasets import Dataset\n",
        "from sklearn.model_selection import train_test_split\n",
        "import torch\n",
        "\n",
        "# Load a pre-trained model and tokenizer\n",
        "model_name = \"EleutherAI/gpt-neo-125M\"  # or \"gpt2\"\n",
        "model = AutoModelForCausalLM.from_pretrained(model_name)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "# Load your formatted dataset\n",
        "import json\n",
        "with open(\"daily_dialog_formatted.json\", \"r\") as f:\n",
        "    data = json.load(f)\n",
        "\n",
        "# Convert to a Hugging Face Dataset\n",
        "dataset = Dataset.from_list(data)\n",
        "\n",
        "# Remove rows with incomplete data\n",
        "dataset = dataset.filter(lambda sample: sample[\"prompt\"] and sample[\"response\"])\n",
        "\n",
        "# Print dataset schema and a sample entry\n",
        "print(dataset)\n",
        "print(dataset[0])\n",
        "\n",
        "# Tokenize the dataset\n",
        "def tokenize_data(sample):\n",
        "    input_text = f\"Prompt: {sample['prompt']}\\nResponse: {sample['response']}\\n\"\n",
        "    tokenized = tokenizer(input_text, truncation=True, padding=\"max_length\", max_length=1000, padding_side=\"right\", return_tensors='pt')\n",
        "    return {\n",
        "        \"input_ids\": tokenized[\"input_ids\"],\n",
        "        \"attention_mask\": tokenized[\"attention_mask\"]\n",
        "    }\n",
        "\n",
        "# Assign a padding token\n",
        "if tokenizer.pad_token is None:\n",
        "    tokenizer.pad_token = tokenizer.eos_token  # Use the end-of-sequence token as the padding token\n",
        "\n",
        "tokenizer.add_special_tokens({'pad_token': '[PAD]'})\n",
        "\n",
        "# Resize model embeddings if needed to include padding token ID\n",
        "model.resize_token_embeddings(len(tokenizer))\n",
        "\n",
        "# Apply tokenization\n",
        "tokenized_dataset = dataset.map(tokenize_data, batched=True, remove_columns=dataset.column_names)\n",
        "\n",
        "# Define training arguments\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./sakhi_model\",\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    learning_rate=1e-5,\n",
        "    per_device_train_batch_size=2,\n",
        "    per_device_eval_batch_size=2,\n",
        "    gradient_accumulation_steps=4,  # Corrected argument name\n",
        "    fp16=True,\n",
        "    num_train_epochs=3,\n",
        "    save_steps=500,\n",
        "    save_total_limit=2,\n",
        "    logging_dir=\"./logs\",\n",
        "    logging_steps=10,\n",
        "    logging_first_step=True,\n",
        "    # Add the prediction_loss_only argument and set it to True\n",
        "    prediction_loss_only=True\n",
        ")\n",
        "\n",
        "# Split the dataset into training and evaluation\n",
        "# Use select to split dataset instead of sklearn.model_selection import train_test_split\n",
        "train_data = tokenized_dataset.select(range(10))  # Assign tokenized_dataset to train_data\n",
        "eval_data = tokenized_dataset.select(range(10, 12))\n",
        "\n",
        "# Define the data collator\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
        "\n",
        "# Disable wandb if not needed\n",
        "os.environ[\"WANDB_DISABLED\"] = \"true\"\n",
        "\n",
        "# Define the Trainer\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_data,\n",
        "    eval_dataset=eval_data,  # Pass the evaluation dataset\n",
        "    tokenizer=tokenizer,\n",
        "    data_collator=data_collator\n",
        ")\n",
        "\n",
        "import pickle\n",
        "\n",
        "with open(\"trainer.pkl\", \"wb\") as f:\n",
        "    pickle.dump(trainer, f)\n",
        "\n",
        "# Reload later\n",
        "with open(\"trainer.pkl\", \"rb\") as f:\n",
        "    trainer = pickle.load(f)\n",
        "# Define a custom compute_loss function\n",
        "def compute_loss(model, inputs, return_outputs=False, num_items_in_batch=None): # Add num_items_in_batch argument\n",
        "    \"\"\"\n",
        "    This function calculates the loss during training.\n",
        "    It's designed for causal language modeling where we want to predict the next token.\n",
        "    \"\"\"\n",
        "    outputs = model(**inputs)  # Get model predictions\n",
        "    # The loss is usually the cross-entropy between predicted logits and target labels\n",
        "    # For causal LM, the target labels are the shifted input_ids\n",
        "    lm_logits = outputs.logits\n",
        "    shift_logits = lm_logits[..., :-1, :].contiguous()\n",
        "    shift_labels = inputs[\"input_ids\"][..., 1:].contiguous()\n",
        "    # Calculate the loss using cross-entropy\n",
        "    loss_fct = torch.nn.CrossEntropyLoss()\n",
        "    loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))\n",
        "    return (loss, outputs) if return_outputs else loss\n",
        "\n",
        "# Set the compute_loss function in the Trainer\n",
        "trainer.compute_loss = compute_loss\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240,
          "referenced_widgets": [
            "4ab4ecbcf19e47fd95fa71d5aaa1b14a",
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            "480113eeb8fb4370b6de51e837a49398"
          ]
        },
        "id": "lkxsdAEtNS2e",
        "outputId": "44a69700-87da-4cb0-c9d7-26f768923999"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/11118 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4ab4ecbcf19e47fd95fa71d5aaa1b14a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['dialog', 'act', 'emotion', 'prompt', 'response'],\n",
            "    num_rows: 11118\n",
            "})\n",
            "{'dialog': ['Say , Jim , how about going for a few beers after dinner ? ', ' You know that is tempting but is really not good for our fitness . ', ' What do you mean ? It will help us to relax . ', \" Do you really think so ? I don't . It will just make us fat and act silly . Remember last time ? \", \" I guess you are right.But what shall we do ? I don't feel like sitting at home . \", ' I suggest a walk over to the gym where we can play singsong and meet some of our friends . ', \" That's a good idea . I hear Mary and Sally often go there to play pingpong.Perhaps we can make a foursome with them . \", ' Sounds great to me ! If they are willing , we could ask them to go dancing with us.That is excellent exercise and fun , too . ', \" Good.Let ' s go now . \", ' All right . '], 'act': [3, 4, 2, 2, 2, 3, 4, 1, 3, 4], 'emotion': [0, 0, 0, 0, 0, 0, 4, 4, 4, 4], 'prompt': 'Say , Jim , how about going for a few beers after dinner ? ', 'response': ' You know that is tempting but is really not good for our fitness . '}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/11118 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e542a3b155e94091899bff8e4e07f3d8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n",
            "<ipython-input-7-3a884f21dadd>:79: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n",
            "  trainer = Trainer(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train(resume_from_checkpoint=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "ABcTSlzGGcLb",
        "outputId": "d4878657-fdc4-42ff-f1e5-2d7b125a47ee"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='3' max='3' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [3/3 05:41, Epoch 1/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>3.159200</td>\n",
              "      <td>No log</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=3, training_loss=2.345799684524536, metrics={'train_runtime': 558.9598, 'train_samples_per_second': 0.054, 'train_steps_per_second': 0.005, 'total_flos': 9183062016000.0, 'train_loss': 2.345799684524536, 'epoch': 1.8})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_pretrained(\"./sakhi_model\")\n",
        "tokenizer.save_pretrained(\"./sakhi_model\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oV0_JoTemdSf",
        "outputId": "c1588201-d91b-4297-e581-562a721fa330"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('./sakhi_model/tokenizer_config.json',\n",
              " './sakhi_model/special_tokens_map.json',\n",
              " './sakhi_model/vocab.json',\n",
              " './sakhi_model/merges.txt',\n",
              " './sakhi_model/added_tokens.json',\n",
              " './sakhi_model/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "chat_pipeline = pipeline(\"text-generation\", model=\"./sakhi_model\", tokenizer=\"./sakhi_model\")\n",
        "response = chat_pipeline(\"Hello, how are you?\", max_length=50)\n",
        "print(response)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QmXU42ObtH2J",
        "outputId": "5dbebcfe-f4e4-459c-b122-5c3ca297c221"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Device set to use cpu\n",
            "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'generated_text': \"Hello, how are you?\\n\\nI'm a newbie to the world of web development. I'm a newbie to web development. I'm a newbie to web development. I'm a newbie to web development. I'm a\"}]\n"
          ]
        }
      ]
    }
  ]
}