src/generate_LaBSE_embeddings_similarity.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eq2VKZANXlKm"
      },
      "source": [
        "# LaBASE filterting application - for final AdvNLP project\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VpI17PoiRS63"
      },
      "outputs": [],
      "source": [
        "from transformers import BertTokenizer, BertModel\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "import torch\n",
        "import pandas as pd\n",
        "from tqdm import tqdm"
      ]
    },
    {
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        "outputId": "da404e42-3f3d-47c6-ea49-f3308079cadd"
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1-M187jqKwBmTWHX4wyRvglYx6vjOe-nz\n",
            "To: /content/medical_corpus_clean.tsv\n",
            "100% 237M/237M [00:04<00:00, 53.7MB/s]\n"
          ]
        }
      ],
      "source": [
        "# Upload data\n",
        "file_id = '1-M187jqKwBmTWHX4wyRvglYx6vjOe-nz'\n",
        "!gdown --id $file_id\n",
        "\n",
        "# Load the TSV file into a Pandas DataFrame\n",
        "df = pd.read_csv(\"medical_corpus_clean.tsv\", sep=',')"
      ]
    },
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      "source": [
        "# Download the model and tokenizer from the LaBSE in transformers library\n",
        "model_name = \"sentence-transformers/LaBSE\"\n",
        "tokenizer = BertTokenizer.from_pretrained(model_name)\n",
        "model = BertModel.from_pretrained(model_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-LnQ66Bglc0r",
        "outputId": "cfc9a7d2-7e32-4de0-cbc6-6a3bfea34501"
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        {
          "data": {
            "text/plain": [
              "BertModel(\n",
              "  (embeddings): BertEmbeddings(\n",
              "    (word_embeddings): Embedding(501153, 768, padding_idx=0)\n",
              "    (position_embeddings): Embedding(512, 768)\n",
              "    (token_type_embeddings): Embedding(2, 768)\n",
              "    (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "    (dropout): Dropout(p=0.1, inplace=False)\n",
              "  )\n",
              "  (encoder): BertEncoder(\n",
              "    (layer): ModuleList(\n",
              "      (0-11): 12 x BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          (intermediate_act_fn): GELUActivation()\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (pooler): BertPooler(\n",
              "    (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "    (activation): Tanh()\n",
              "  )\n",
              ")"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Assign device\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# Move model to CUDA if available\n",
        "model.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_zPtB6Lbmf_D"
      },
      "outputs": [],
      "source": [
        "import torch.nn.functional as F\n",
        "\n",
        "def cosine_similarity_custom(x, y):\n",
        "    # Calculate cosine similarity\n",
        "    dot_product = torch.sum(x * y, dim=-1)\n",
        "    norm_x = torch.norm(x, dim=-1)\n",
        "    norm_y = torch.norm(y, dim=-1)\n",
        "\n",
        "    similarity = dot_product / (norm_x * norm_y)\n",
        "\n",
        "    return similarity\n",
        "\n",
        "def filter_bilingual_corpus_batched(source_sentences, target_sentences, sentence_ids, batch_size=16):\n",
        "    similarity_metric = []\n",
        "    sentence_pairs_ids = []\n",
        "\n",
        "    # Calculate the total number of sentence pairs\n",
        "    total_pairs = len(source_sentences)\n",
        "\n",
        "    # Iterate through sentence pairs with tqdm for the progress bar\n",
        "    for i in tqdm(range(0, total_pairs, batch_size), desc=\"Processing Batches\"):\n",
        "        batch_sources = source_sentences[i:i + batch_size]\n",
        "        batch_targets = target_sentences[i:i + batch_size]\n",
        "        batch_ids = sentence_ids[i:i + batch_size]\n",
        "\n",
        "        # Tokenize and generate embeddings for source and target sentences\n",
        "        source_tokens = tokenizer(batch_sources, return_tensors=\"pt\", padding=True, truncation=True).to(device)\n",
        "        target_tokens = tokenizer(batch_targets, return_tensors=\"pt\", padding=True, truncation=True).to(device)\n",
        "\n",
        "        with torch.no_grad():\n",
        "            source_embedding_mean = model(**source_tokens).last_hidden_state.mean(dim=1)\n",
        "            target_embedding_mean = model(**target_tokens).last_hidden_state.mean(dim=1)\n",
        "\n",
        "        # Calculate similarity between corresponding embeddings\n",
        "        similarities = cosine_similarity_custom(source_embedding_mean, target_embedding_mean)\n",
        "\n",
        "        # Take the similarity score for each sentence pair within the batch\n",
        "        for j, similarity in enumerate(similarities.cpu().numpy()):\n",
        "            similarity_metric.append(similarity)\n",
        "            # Save the common pair of sentence IDs for the entire batch\n",
        "            sentence_pairs_ids.append(batch_ids[j])\n",
        "\n",
        "    return similarity_metric, sentence_pairs_ids"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8tMSi-W7tOZH",
        "outputId": "fc1a69f0-95ab-4d72-8e3e-c2c65f3c9ced"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Processing Batches: 100%|██████████| 134182/134182 [2:22:02<00:00, 15.74it/s]\n"
          ]
        }
      ],
      "source": [
        "# Use functions to generate similarity measures for the sentence pairs\n",
        "similarity_metric, sentence_pairs_ids = filter_bilingual_corpus_batched(df['pol'].tolist(), df['eng'].tolist(), df['id'].tolist(), batch_size=8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "16fbVptlGllj"
      },
      "outputs": [],
      "source": [
        "# Create a DataFrame\n",
        "output_df = pd.DataFrame({'id': sentence_pairs_ids,'score': similarity_metric})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
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        "id": "Jj4RrcZvHOIC",
        "outputId": "d6e2fb92-97c8-477a-f971-a63680688bac"
      },
      "outputs": [
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        "# Save dataframe as csv\n",
        "output_df.to_csv('LaBSE_scores.csv', index=False)"
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