BERT/Sbert_finetune.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "### FineTune SBERT with custom data"
      ],
      "metadata": {
        "id": "K4z97NSRor27"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sentence_transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aAwptORdMYXp",
        "outputId": "eb4bfee4-b4b8-4b17-9e7c-d502c241f68d"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting sentence_transformers\n",
            "  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.0/86.0 KB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting transformers<5.0.0,>=4.6.0\n",
            "  Downloading transformers-4.26.1-py3-none-any.whl (6.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.3/6.3 MB\u001b[0m \u001b[31m45.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from sentence_transformers) (4.64.1)\n",
            "Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.8/dist-packages (from sentence_transformers) (1.13.1+cu116)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.8/dist-packages (from sentence_transformers) (0.14.1+cu116)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from sentence_transformers) (1.22.4)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.8/dist-packages (from sentence_transformers) (1.2.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from sentence_transformers) (1.10.1)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.8/dist-packages (from sentence_transformers) (3.7)\n",
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.97-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m64.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting huggingface-hub>=0.4.0\n",
            "  Downloading huggingface_hub-0.12.1-py3-none-any.whl (190 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.3/190.3 KB\u001b[0m \u001b[31m22.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from huggingface-hub>=0.4.0->sentence_transformers) (2.25.1)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub>=0.4.0->sentence_transformers) (23.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub>=0.4.0->sentence_transformers) (4.5.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from huggingface-hub>=0.4.0->sentence_transformers) (3.9.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub>=0.4.0->sentence_transformers) (6.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers<5.0.0,>=4.6.0->sentence_transformers) (2022.6.2)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m82.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: joblib in /usr/local/lib/python3.8/dist-packages (from nltk->sentence_transformers) (1.2.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.8/dist-packages (from nltk->sentence_transformers) (8.1.3)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn->sentence_transformers) (3.1.0)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.8/dist-packages (from torchvision->sentence_transformers) (8.4.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface-hub>=0.4.0->sentence_transformers) (2022.12.7)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface-hub>=0.4.0->sentence_transformers) (1.26.14)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface-hub>=0.4.0->sentence_transformers) (4.0.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface-hub>=0.4.0->sentence_transformers) (2.10)\n",
            "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=125938 sha256=f5a39298b5e25c44b0a189bf69e27916ae805f2f09470e5d058a28322478744f\n",
            "  Stored in directory: /root/.cache/pip/wheels/5e/6f/8c/d88aec621f3f542d26fac0342bef5e693335d125f4e54aeffe\n",
            "Successfully built sentence_transformers\n",
            "Installing collected packages: tokenizers, sentencepiece, huggingface-hub, transformers, sentence_transformers\n",
            "Successfully installed huggingface-hub-0.12.1 sentence_transformers-2.2.2 sentencepiece-0.1.97 tokenizers-0.13.2 transformers-4.26.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "2_9g08KGMPH4"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "import math\n",
        "from sentence_transformers import SentenceTransformer, LoggingHandler, losses, util, InputExample\n",
        "from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator\n",
        "import logging\n",
        "from datetime import datetime\n",
        "import os\n",
        "import gzip\n",
        "import csv"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#### Just some code to print debug information to stdout\n",
        "logging.basicConfig(format='%(asctime)s - %(message)s',\n",
        "                    datefmt='%Y-%m-%d %H:%M:%S',\n",
        "                    level=logging.INFO,\n",
        "                    handlers=[LoggingHandler()])\n",
        "#### /print debug information to stdout"
      ],
      "metadata": {
        "id": "i3FFXUQdMhzq"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Check if dataset exsist. If not, download and extract  it\n",
        "sts_dataset_path = 'datasets/stsbenchmark.tsv.gz'\n",
        "\n",
        "if not os.path.exists(sts_dataset_path):\n",
        "    util.http_get('https://sbert.net/datasets/stsbenchmark.tsv.gz', sts_dataset_path)"
      ],
      "metadata": {
        "colab": {
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        "outputId": "683fcf1e-a1da-466b-9a4d-8588255d93fe"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/392k [00:00<?, ?B/s]"
            ],
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    },
    {
      "cell_type": "code",
      "source": [
        "# Read the dataset\n",
        "model_name = 'nli-distilroberta-base-v2'\n",
        "train_batch_size = 16\n",
        "num_epochs = 4\n",
        "model_save_path = 'output/training_stsbenchmark_continue_training-'+model_name+'-'+datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\")\n",
        "\n"
      ],
      "metadata": {
        "id": "V8OhS1y0MSVQ"
      },
      "execution_count": 6,
      "outputs": []
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    {
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        "# Load a pre-trained sentence transformer model\n",
        "model = SentenceTransformer(model_name)\n",
        "\n",
        "# Convert the dataset to a DataLoader ready for training\n",
        "logging.info(\"Read STSbenchmark train dataset\")"
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    {
      "cell_type": "code",
      "source": [
        "train_samples = []\n",
        "dev_samples = []\n",
        "test_samples = []\n",
        "with gzip.open(sts_dataset_path, 'rt', encoding='utf8') as fIn:\n",
        "    reader = csv.DictReader(fIn, delimiter='\\t', quoting=csv.QUOTE_NONE)\n",
        "    for row in reader:\n",
        "        ###We are using three columns 'score', 'sentence1' and 'sentence2'\n",
        "        score = float(row['score']) / 5.0  # Normalize score to range 0 ... 1\n",
        "        inp_example = InputExample(texts=[row['sentence1'], row['sentence2']], label=score)\n",
        "        ###Check data to be trained or tested here\n",
        "        if row['split'] == 'dev':\n",
        "            dev_samples.append(inp_example)\n",
        "        elif row['split'] == 'test':\n",
        "            test_samples.append(inp_example)\n",
        "        else:\n",
        "            train_samples.append(inp_example)"
      ],
      "metadata": {
        "id": "QU3MPNXAMxER"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataloader = DataLoader(train_samples, shuffle=True, batch_size=train_batch_size)\n",
        "train_loss = losses.CosineSimilarityLoss(model=model)"
      ],
      "metadata": {
        "id": "Pqxu5FYMM7Tn"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Development set: Measure correlation between cosine score and gold labels\n",
        "logging.info(\"Read STSbenchmark dev dataset\")\n",
        "evaluator = EmbeddingSimilarityEvaluator.from_input_examples(dev_samples, name='sts-dev')\n"
      ],
      "metadata": {
        "id": "00233TvJNEa4"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Configure the training. We skip evaluation in this example\n",
        "warmup_steps = math.ceil(len(train_dataloader) * num_epochs * 0.1) #10% of train data for warm-up\n",
        "logging.info(\"Warmup-steps: {}\".format(warmup_steps))\n"
      ],
      "metadata": {
        "id": "KS2yVtcbNEdp"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "model.fit(train_objectives=[(train_dataloader, train_loss)],\n",
        "          evaluator=evaluator,\n",
        "          epochs=num_epochs,\n",
        "          evaluation_steps=1000,\n",
        "          warmup_steps=warmup_steps,\n",
        "          output_path=model_save_path)\n"
      ],
      "metadata": {
        "id": "NHqCSkI3OYpN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "###Check sample data from \n",
        "for test_images, test_labels in train_dataloader:  \n",
        "    sample_image = test_images[0]    # Reshape them according to your needs.\n",
        "    sample_label = test_labels[0]\n",
        "print(sample_image,sample_label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k2wdf2JKObO1",
        "outputId": "93be6204-babc-4108-d525-b21b69ef7c85"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': tensor([[    0,   250,  8978,     8,  1104,  4758, 15028,    15,    10,  9219,\n",
            "          3428,     4,     2,     1,     1,     1],\n",
            "        [    0, 37426,  3314,   154,    16,   542,  7904,  3320,   868,     4,\n",
            "             2,     1,     1,     1,     1,     1],\n",
            "        [    0,   133,   693,  1826,    10,   909, 15811,    16,  7893,    10,\n",
            "         16596,     4,     2,     1,     1,     1],\n",
            "        [    0,   500, 14963,  8388, 18216,   108,  4315,     6,  9895,  4630,\n",
            "          2613,   756,    16,    81,     4,     2],\n",
            "        [    0,   250,   909,     8,  1104,  1345,     9,    41,  5802,  2341,\n",
            "          1992,     4,     2,     1,     1,     1]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n",
            "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n",
            "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n",
            "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
            "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]])} tensor(0.6000)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "###Check model performance\n",
        "model = SentenceTransformer(model_save_path)\n",
        "test_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(test_samples, name='sts-test')\n",
        "test_evaluator(model, output_path=model_save_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e5FZ5qMtnwkX",
        "outputId": "666b9a4e-fc27-4ba1-c9e3-bdc5af73f515"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8531391746098621"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nacaY8_9n2v1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "REFERENCES : https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/sts/training_stsbenchmark_continue_training.py"
      ],
      "metadata": {
        "id": "t1qCvSRoMU-p"
      }
    }
  ]
}