{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h1sqXqJLgHSt"
      },
      "source": [
        "## all code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ouQwlB2dpP6",
        "outputId": "6f54b797-1641-4446-ae38-ab1998e25086"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Building wheels for collected packages: rouge-score\n",
            "  Building wheel for rouge-score (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for rouge-score: filename=rouge_score-0.1.2-py3-none-any.whl size=24954 sha256=54e96fad82c10733c163631dc371123119d424e17174271813e66e158046b429\n",
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            "Installing collected packages: tokenizers, texttable, brotli, xxhash, pyzstd, pytesseract, pyppmd, pycryptodomex, pybcj, multivolumefile, multidict, inflate64, frozenlist, dill, async-timeout, yarl, rouge-score, responses, py7zr, multiprocess, huggingface-hub, aiosignal, transformers, aiohttp, tensorboard, datasets\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.12.2\n",
            "    Uninstalling tensorboard-2.12.2:\n",
            "      Successfully uninstalled tensorboard-2.12.2\n",
            "\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",
            "tensorflow 2.12.0 requires tensorboard<2.13,>=2.12, but you have tensorboard 2.13.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 brotli-1.0.9 datasets-2.12.0 dill-0.3.6 frozenlist-1.3.3 huggingface-hub-0.14.1 inflate64-0.3.1 multidict-6.0.4 multiprocess-0.70.14 multivolumefile-0.2.3 py7zr-0.20.5 pybcj-1.0.1 pycryptodomex-3.17 pyppmd-1.0.0 pytesseract-0.3.10 pyzstd-0.15.7 responses-0.18.0 rouge-score-0.1.2 tensorboard-2.13.0 texttable-1.6.7 tokenizers-0.13.3 transformers-4.28.1 xxhash-3.2.0 yarl-1.9.2\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "git-lfs is already the newest version (2.9.2-1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 24 not upgraded.\n"
          ]
        }
      ],
      "source": [
        "!pip install pytesseract transformers datasets rouge-score nltk tensorboard py7zr --upgrade\n",
        "# install git-fls for pushing model and logs to the hugging face hub\n",
        "!sudo apt-get install git-lfs --yes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "worbM_RqgGY4"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415,
          "referenced_widgets": [
            "9f925c332486440da0d27b90dd1ee97e",
            "802bc61b49fc4207ba76fa0f722ef6f9",
            "614875cae43a4b03b8f962fbb6a7d4d3",
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            "20383f1f70da436ab5142661fcb2544f",
            "632619bfaf774a8d8aef3595431ee8f3"
          ]
        },
        "id": "-oUYlSlLdrCR",
        "outputId": "8f68cdc2-1275-4e28-8b1d-063a3be6da8d"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9f925c332486440da0d27b90dd1ee97e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h59FXOt1eGWI"
      },
      "outputs": [],
      "source": [
        "dataset_id = \"abhijitgayen/cogoport_bot_chat\"\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233,
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        },
        "id": "VP8zAtvieGwV",
        "outputId": "c9c8bd38-76e9-491f-b766-8741ea594b4a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0c904e69e1eb4de2ae1a94739d20a804",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/23.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset json/abhijitgayen--cogoport_bot_chat to /root/.cache/huggingface/datasets/abhijitgayen___json/abhijitgayen--cogoport_bot_chat-a2198e2e6a513f2b/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fa016626053d46ae98ae1511908f6f3d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "48e32124334b430faec34c49b0677e48",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/64.0k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c4b956514ebe47ae838c989aa7cfa2f1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9d9dc0c50b7d41268c3c56cc4124bfb0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/abhijitgayen___json/abhijitgayen--cogoport_bot_chat-a2198e2e6a513f2b/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b2c64ec3cb904b70a25851ce4ef8c5a2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "# Load dataset from the hub\n",
        "dataset = load_dataset(dataset_id)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mrAGFw0Aj1r4",
        "outputId": "bbeddb13-9989-450b-aa19-a2adcb077534"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['admin', 'user'],\n",
              "        num_rows: 209\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "code",
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        },
        "id": "yjLxY4bNeOcd",
        "outputId": "57c8aff1-df60-4335-a088-1ec420a1a7d6"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0a92a8dfcbf54e0c9ffe112c99a91994",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/1.15k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d2f2a1a78d104448ad0f8cde143bb6aa",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.57k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9cdfa721f22e48ec85226d37a0a444ec",
              "version_major": 2,
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            },
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/127k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7d005bba8e34453d8c2092320dce0ddc",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/62.9k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2ab24813262b4f5dbff563c363d02c6e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)in/added_tokens.json:   0%|          | 0.00/16.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8b8864556f404045941cf8c72b440cbc",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/772 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n",
        "\n",
        "model_id=\"facebook/blenderbot-400M-distill\"\n",
        "\n",
        "# Load tokenizer of FLAN-t5-base\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "ZyM-P1PYeRJp",
        "outputId": "bf50e044-5642-42a7-bc5e-1ad1bd4d5208"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b900230a0fe54376958ebec2c0641910",
              "version_major": 2,
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            },
            "text/plain": [
              "Map:   0%|          | 0/209 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Max source length: 65\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5310a520cf3d4a48aefd44c4a1654944",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/209 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Max target length: 123\n"
          ]
        }
      ],
      "source": [
        "# The maximum total input sequence length after tokenization.\n",
        "# Sequences longer than this will be truncated, sequences shorter will be padded.\n",
        "tokenized_inputs = dataset.map(lambda x: tokenizer(x[\"user\"], truncation=True), batched=True, remove_columns=[\"user\", \"admin\"])\n",
        "max_source_length = max([len(x) for x in tokenized_inputs['train'][\"input_ids\"]])\n",
        "print(f\"Max source length: {max_source_length}\")\n",
        "\n",
        "# The maximum total sequence length for target text after tokenization.\n",
        "# Sequences longer than this will be truncated, sequences shorter will be padded.\"\n",
        "tokenized_targets = dataset.map(lambda x: tokenizer(x[\"admin\"], truncation=True), batched=True, remove_columns=[\"user\", \"admin\"])\n",
        "max_target_length = max([len(x) for x in tokenized_targets['train'][\"input_ids\"]])\n",
        "print(f\"Max target length: {max_target_length}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35,
          "referenced_widgets": [
            "4ebd7b31d3b8426492337c2e5910e638",
            "8f7bc7b68f5a4764a3827c1ae63e32ca",
            "f1b9836e268243e8aea8e7527aa7250a",
            "870ae45faca14d95aa9604047c3958eb",
            "fd722b48b23247fdab4d9a9220458d17",
            "313114e7961c4555ace9aa28b4b20ee7",
            "eb134c41fc714901af1b60513a991c94",
            "88530dd0907f46a8aefb3e39992470e9",
            "246a36c3ede9451398958d9f30de513c",
            "68a85d47f6d84362826779a7f09b04e3",
            "911900d3457f408a9fcde2997d2671f9"
          ]
        },
        "id": "_A-S3UQ7eYJw",
        "outputId": "ba777f21-670d-4131-9359-c8d73ede9ea0"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4ebd7b31d3b8426492337c2e5910e638",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/209 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Keys of tokenized dataset: ['input_ids', 'attention_mask', 'labels']\n"
          ]
        }
      ],
      "source": [
        "def preprocess_function(sample,padding=\"max_length\"):\n",
        "    # add prefix to the input for t5\n",
        "    inputs = [\"admin: \" + item for item in sample[\"user\"]]\n",
        "\n",
        "    # tokenize inputs\n",
        "    model_inputs = tokenizer(inputs, max_length=max_source_length, padding=padding, truncation=True)\n",
        "\n",
        "    # Tokenize targets with the `text_target` keyword argument\n",
        "    labels = tokenizer(text_target=sample[\"admin\"], max_length=max_target_length, padding=padding, truncation=True)\n",
        "\n",
        "    # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore\n",
        "    # padding in the loss.\n",
        "    if padding == \"max_length\":\n",
        "        labels[\"input_ids\"] = [\n",
        "            [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels[\"input_ids\"]\n",
        "        ]\n",
        "\n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs\n",
        "\n",
        "tokenized_dataset = dataset.map(preprocess_function, batched=True, remove_columns=[\"user\", \"admin\"])\n",
        "print(f\"Keys of tokenized dataset: {list(tokenized_dataset['train'].features)}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "fb62505697684923ae1f25ec5755ff18",
            "47207d7f181c4753a78d27d53b0f696b",
            "ae11ca5774aa45d48e3d4e1df32e457c",
            "073770713f534358b37357b766dd17f0",
            "52767343614d47a987e4fe313de6e529",
            "c8af7d0a10d341188308ed84678efa38",
            "8bc6edee8a004678930d93d00428c5de",
            "51d6c66025104f0ab0e5ab86dd85b550",
            "d7ff4c6cdcc948438b37d2e1853610ce",
            "f685eedf0e634c97b55c90daad9f96c0",
            "2ee9ff4ce3e54a758b7d8c1a02f62d63",
            "eea9d766b18c40cf9f7f3346c8fbc862",
            "434ec088086e482dbcb3cd91a537421d",
            "7d9465dcc5c04f5e887ae10f949e5a76",
            "0432325e44704277962e866914989fff",
            "7d5ecfc6e282475db565abfe65a40dfe",
            "e663517d5db94b37bde1fe64367f2abb",
            "17d7918e5c4a423791c9fbf0e3736356",
            "fc83f99ee8e442e09ce8830f15b9fb02",
            "c6f015ef26ee4f51920ebad856c1e2d3",
            "7656b8c7f0ac494aa6be455bc8f0fe79",
            "a816034acc1549c69f1621fc70ee5004"
          ]
        },
        "id": "OYhAMyrrfhLJ",
        "outputId": "ee06f436-97fc-45d8-f5c0-0b0f2d52ddc4"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fb62505697684923ae1f25ec5755ff18",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/730M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "eea9d766b18c40cf9f7f3346c8fbc862",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/347 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from transformers import AutoModelForSeq2SeqLM\n",
        "\n",
        "# huggingface hub model id\n",
        "model_id=\"facebook/blenderbot-400M-distill\"\n",
        "\n",
        "# load model from the hub\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_id)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ETL75G9deq_g",
        "outputId": "95ebf75e-63e3-4da0-9ce2-e2869bcdc364"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting evaluate\n",
            "  Downloading evaluate-0.4.0-py3-none-any.whl (81 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.4/81.4 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: responses<0.19 in /usr/local/lib/python3.10/dist-packages (from evaluate) (0.18.0)\n",
            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from evaluate) (0.70.14)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from evaluate) (1.5.3)\n",
            "Requirement already satisfied: fsspec[http]>=2021.05.0 in /usr/local/lib/python3.10/dist-packages (from evaluate) (2023.4.0)\n",
            "Requirement already satisfied: huggingface-hub>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from evaluate) (0.14.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from evaluate) (23.1)\n",
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            "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets>=2.0.0->evaluate) (9.0.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets>=2.0.0->evaluate) (6.0)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets>=2.0.0->evaluate) (3.8.4)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.7.0->evaluate) (4.5.0)\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->evaluate) (3.4)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->evaluate) (1.26.15)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->evaluate) (2022.7.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->evaluate) (2.8.2)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.3.3)\n",
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            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (4.0.2)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (6.0.4)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (23.1.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->evaluate) (1.16.0)\n",
            "Installing collected packages: evaluate\n",
            "Successfully installed evaluate-0.4.0\n"
          ]
        }
      ],
      "source": [
        "!pip install evaluate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85,
          "referenced_widgets": [
            "a929c0632eec4e7da4e2560232d4436d",
            "1f40e4741d6d45a5b899a2da0df4f5c4",
            "88900ff3317a4c8dbe73e756ae8acfcd",
            "09e19798a8b546759a93c3727e532f68",
            "d97d0dde4be440969f109a5c5dad92d7",
            "1970f6cc562b4c508396bdd86e2f5726",
            "7b03b008db344049a9e3fd63501da446",
            "6a71dce757614a29a07238fd15e95c4f",
            "938a5b3380ca42d8b4695b42b03a7409",
            "dc68bee4fab44a02af0ded70489797f1",
            "31feeb9808304c1193faa4f4cc810ab8"
          ]
        },
        "id": "dbPeZCFeej2a",
        "outputId": "7108c137-5050-473f-d1a9-d501eec21e61"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a929c0632eec4e7da4e2560232d4436d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.27k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import evaluate\n",
        "import nltk\n",
        "import numpy as np\n",
        "from nltk.tokenize import sent_tokenize\n",
        "nltk.download(\"punkt\")\n",
        "\n",
        "# Metric\n",
        "metric = evaluate.load(\"rouge\")\n",
        "\n",
        "# helper function to postprocess text\n",
        "def postprocess_text(preds, labels):\n",
        "    preds = [pred.strip() for pred in preds]\n",
        "    labels = [label.strip() for label in labels]\n",
        "\n",
        "    # rougeLSum expects newline after each sentence\n",
        "    preds = [\"\\n\".join(sent_tokenize(pred)) for pred in preds]\n",
        "    labels = [\"\\n\".join(sent_tokenize(label)) for label in labels]\n",
        "\n",
        "    return preds, labels\n",
        "\n",
        "def compute_metrics(eval_preds):\n",
        "    preds, labels = eval_preds\n",
        "    if isinstance(preds, tuple):\n",
        "        preds = preds[0]\n",
        "    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
        "    # Replace -100 in the labels as we can't decode them.\n",
        "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
        "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
        "\n",
        "    # Some simple post-processing\n",
        "    decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)\n",
        "\n",
        "    result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)\n",
        "    result = {k: round(v * 100, 4) for k, v in result.items()}\n",
        "    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]\n",
        "    result[\"gen_len\"] = np.mean(prediction_lens)\n",
        "    return result\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b6xfmT0-elxJ"
      },
      "outputs": [],
      "source": [
        "from transformers import DataCollatorForSeq2Seq\n",
        "\n",
        "# we want to ignore tokenizer pad token in the loss\n",
        "label_pad_token_id = -100\n",
        "# Data collator\n",
        "data_collator = DataCollatorForSeq2Seq(\n",
        "    tokenizer,\n",
        "    model=model,\n",
        "    label_pad_token_id=label_pad_token_id,\n",
        "    pad_to_multiple_of=8\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xzXqP2tQeysZ"
      },
      "outputs": [],
      "source": [
        "from huggingface_hub import HfFolder\n",
        "from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments\n",
        "\n",
        "# Hugging Face repository id\n",
        "repository_id = \"abhijitgayen/cogo-blenderbot\"\n",
        "\n",
        "# Define training args\n",
        "training_args = Seq2SeqTrainingArguments(\n",
        "    output_dir=repository_id,\n",
        "    per_device_train_batch_size=8,\n",
        "    per_device_eval_batch_size=8,\n",
        "    predict_with_generate=True,\n",
        "    fp16=False, # Overflows with fp16\n",
        "    learning_rate=5e-5,\n",
        "    num_train_epochs=5,\n",
        "    # logging & evaluation strategies\n",
        "    logging_dir=f\"{repository_id}/logs\",\n",
        "    logging_strategy=\"steps\",\n",
        "    logging_steps=500,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    save_total_limit=2,\n",
        "    load_best_model_at_end=True,\n",
        "    # metric_for_best_model=\"overall_f1\",\n",
        "    # push to hub parameters\n",
        "    report_to=\"tensorboard\",\n",
        "    push_to_hub=False,\n",
        "    hub_strategy=\"every_save\",\n",
        "    hub_model_id=repository_id,\n",
        "    hub_token=HfFolder.get_token(),\n",
        ")\n",
        "\n",
        "# Create Trainer instance\n",
        "trainer = Seq2SeqTrainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    data_collator=data_collator,\n",
        "    train_dataset=tokenized_dataset[\"train\"],\n",
        "    eval_dataset=tokenized_dataset[\"train\"],\n",
        "    compute_metrics=compute_metrics,\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "id": "lGcjppXOft-s",
        "outputId": "86bb9e0a-4395-4e17-f384-df8bfe842247"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "You're using a BlenderbotTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='28' max='135' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 28/135 16:33 < 1:08:09, 0.03 it/s, Epoch 1/5]\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",
              "  </tbody>\n",
              "</table><p>\n",
              "    <div>\n",
              "      \n",
              "      <progress value='19' max='27' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [19/27 27:20 < 12:09, 0.01 it/s]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='135' max='135' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [135/135 4:47:08, Epoch 5/5]\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",
              "      <th>Rouge1</th>\n",
              "      <th>Rouge2</th>\n",
              "      <th>Rougel</th>\n",
              "      <th>Rougelsum</th>\n",
              "      <th>Gen Len</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.297410</td>\n",
              "      <td>35.381500</td>\n",
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              "      <td>29.062201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>No log</td>\n",
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              "    <tr>\n",
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              "      <td>No log</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.390345</td>\n",
              "      <td>50.443800</td>\n",
              "      <td>34.599800</td>\n",
              "      <td>45.723800</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>No log</td>\n",
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              "</table><p>"
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              "TrainOutput(global_step=135, training_loss=1.0375973171657986, metrics={'train_runtime': 17293.9048, 'train_samples_per_second': 0.06, 'train_steps_per_second': 0.008, 'total_flos': 159911171481600.0, 'train_loss': 1.0375973171657986, 'epoch': 5.0})"
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          "metadata": {},
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      "source": [
        "# Start training\n",
        "trainer.train()"
      ]
    },
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        },
        "id": "BzzPQZFymUtN",
        "outputId": "2010d0e1-a93e-41ca-ba2c-76f4e4dbf5d3"
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      "outputs": [
        {
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      "execution_count": null,
      "metadata": {
        "colab": {
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      "execution_count": null,
      "metadata": {
        "id": "-yWNsTjO1U-u"
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      "metadata": {
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      "metadata": {
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      ]
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        "from huggingface_hub import HfApi\n",
        "api = HfApi()\n",
        "\n",
        "api.upload_folder(\n",
        "    folder_path=\"/content/my_model\",\n",
        "    repo_id=repository_id,\n",
        "    repo_type= \"model\",\n",
        ")"
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