code/sentbert_pp_finetune.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "## This is the code written to fine-tune Sentbert-PP model on PARADE dataset\n",
        "\n",
        "# Please upload PARADE_dataset-main.zip to colab notebook, zip file present in code base\n",
        "# Code works on GPU"
      ],
      "metadata": {
        "id": "dI5e4spvTMhd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y7WVYRNNef2G"
      },
      "source": [
        "# **Install Libraries and unzip Parade Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LUVC6BCBErGU",
        "outputId": "7364a39d-c3f7-4cdb-d291-72df0423cdc0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.13.4 tokenizers-0.13.3 transformers-4.27.4\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Building wheels for collected packages: sentence-transformers\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125942 sha256=4eaabdd8dbf6d193a1ce31193dfb67a3ffe0215f0e7ed1d37367a250f40e9f49\n",
            "  Stored in directory: /root/.cache/pip/wheels/71/67/06/162a3760c40d74dd40bc855d527008d26341c2b0ecf3e8e11f\n",
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            "Installing collected packages: sentencepiece, sentence-transformers\n",
            "Successfully installed sentence-transformers-2.2.2 sentencepiece-0.1.97\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers\n",
        "!pip install -U sentence-transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FP8DcANwz331",
        "outputId": "28127ec1-a3c3-4f74-c4f0-9a2e2d91ddd7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/transformers/generation_utils.py:24: FutureWarning: Importing `GenerationMixin` from `src/transformers/generation_utils.py` is deprecated and will be removed in Transformers v5. Import as `from transformers import GenerationMixin` instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/transformers/generation_tf_utils.py:24: FutureWarning: Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/transformers/generation_flax_utils.py:24: FutureWarning: Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "from transformers import *\n",
        "import torch\n",
        "from sentence_transformers import SentenceTransformer, LoggingHandler, losses, InputExample\n",
        "from torch.utils.data import DataLoader\n",
        "from sklearn.utils import shuffle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bw2hJPPqHeUl",
        "outputId": "b9bce7cb-8dc1-4e44-c09c-9719a1d81f04"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  PARADE_dataset-main.zip\n",
            "   creating: PARADE_dataset-main/\n",
            "  inflating: PARADE_dataset-main/PARADE_test.txt  \n",
            "  inflating: PARADE_dataset-main/PARADE_train.txt  \n",
            "  inflating: PARADE_dataset-main/PARADE_validation.txt  \n",
            "  inflating: PARADE_dataset-main/README.md  \n"
          ]
        }
      ],
      "source": [
        "!unzip PARADE_dataset-main.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Preprocess PARADE Dataset**"
      ],
      "metadata": {
        "id": "g21_xoSPZrWV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Pvt1D4hWZow4"
      },
      "outputs": [],
      "source": [
        "def pre_process_parade(filename):\n",
        "    with open(filename, 'r') as f:\n",
        "        lines = f.readlines()[1:]\n",
        "        data = {'input1':[], 'input2':[], 'similarity_score':[]}\n",
        "        for line in lines:\n",
        "            split_line = line.split('\\t')\n",
        "            data['input1'].append(split_line[3])\n",
        "            data['input2'].append(split_line[4])\n",
        "            data['similarity_score'].append(int(split_line[1]))\n",
        "            \n",
        "    return data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ANcNCSiIaAqR"
      },
      "outputs": [],
      "source": [
        "train_data_parade = pre_process_parade('./PARADE_dataset-main/PARADE_train.txt')\n",
        "\n",
        "train_data = {'input1':[], 'input2':[], 'similiarity_score':[]}\n",
        "\n",
        "train_data['input1'] = train_data_parade['input1']\n",
        "train_data['input2'] = train_data_parade['input2']\n",
        "train_data['similarity_score'] = train_data_parade['similarity_score']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uj6r-CsdfBeH"
      },
      "source": [
        "# **Finetune Sentbert-PP**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IjLjy_hIjQ6d"
      },
      "outputs": [],
      "source": [
        "def finetune_sentbert_pp():\n",
        "\n",
        "    model = SentenceTransformer('paraphrase-TinyBERT-L6-v2')\n",
        "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "    model.to(device)\n",
        "\n",
        "    train_ex = []\n",
        "    for i in range(len(train_data['input1'])):\n",
        "        train_ex.append(InputExample(texts=[train_data['input1'][i], train_data['input2'][i]], label=train_data['similarity_score'][i]))\n",
        "\n",
        "    train_dataloader = DataLoader(train_ex, shuffle=True, batch_size=32)\n",
        "    train_loss = losses.ContrastiveLoss(model=model)\n",
        "\n",
        "    model.fit([(train_dataloader, train_loss)], epochs=3, show_progress_bar=True)\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        {
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          "text": [
            "loading configuration file /root/.cache/torch/sentence_transformers/sentence-transformers_paraphrase-TinyBERT-L6-v2/config.json\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"/root/.cache/torch/sentence_transformers/sentence-transformers_paraphrase-TinyBERT-L6-v2/\",\n",
            "  \"architectures\": [\n",
            "    \"BertModel\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 6,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.27.4\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 30522\n",
            "}\n",
            "\n",
            "loading weights file /root/.cache/torch/sentence_transformers/sentence-transformers_paraphrase-TinyBERT-L6-v2/pytorch_model.bin\n",
            "All model checkpoint weights were used when initializing BertModel.\n",
            "\n",
            "All the weights of BertModel were initialized from the model checkpoint at /root/.cache/torch/sentence_transformers/sentence-transformers_paraphrase-TinyBERT-L6-v2/.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use BertModel for predictions without further training.\n",
            "loading configuration file /root/.cache/torch/sentence_transformers/sentence-transformers_paraphrase-TinyBERT-L6-v2/config.json\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"/root/.cache/torch/sentence_transformers/sentence-transformers_paraphrase-TinyBERT-L6-v2/\",\n",
            "  \"architectures\": [\n",
            "    \"BertModel\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 6,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.27.4\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 30522\n",
            "}\n",
            "\n",
            "loading file vocab.txt\n",
            "loading file tokenizer.json\n",
            "loading file added_tokens.json\n",
            "loading file special_tokens_map.json\n",
            "loading file tokenizer_config.json\n",
            "loading configuration file /root/.cache/torch/sentence_transformers/sentence-transformers_paraphrase-TinyBERT-L6-v2/config.json\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"/root/.cache/torch/sentence_transformers/sentence-transformers_paraphrase-TinyBERT-L6-v2/\",\n",
            "  \"architectures\": [\n",
            "    \"BertModel\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 6,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.27.4\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 30522\n",
            "}\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b7fe5cdb3ed24cbcbf461f3c6cf685ac"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Iteration:   0%|          | 0/236 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a50ebf21622d465ca7bc94638ebe9bb9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Iteration:   0%|          | 0/236 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c985ad53504b4185b5de5a5705780bd1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Iteration:   0%|          | 0/236 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "575b7deebb624979bac363b916934188"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "model = finetune_sentbert_pp()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Save the Fine-tuned model to Google Drive**"
      ],
      "metadata": {
        "id": "Uhg6cUVqbWqn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(model.state_dict(),'finetuned-sentbert-pp-parade-train.pt')"
      ],
      "metadata": {
        "id": "kN5U7IHZ8HC3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eP25MvkabLyN",
        "outputId": "6af774c5-8a91-4a87-feba-852b1bee03d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp -r \"/content/finetuned-sentbert-pp-parade-train.pt\" \"/content/drive/MyDrive\""
      ],
      "metadata": {
        "id": "jpYk3b1NbPZH"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "YYuC9nxvfLcj",
        "7G_FAr8mfjKJ",
        "1k45ablN_4Yk",
        "OyaVvC4cfSdT",
        "1CifX-rjvbOs",
        "f8DaqWpq64OU",
        "6XEaSj48xQgb",
        "eBnse3cVxC2a",
        "8lJkfgyvxHlk"
      ],
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
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