{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "byfhCh_mJB_K",
        "outputId": "fe485abb-ca7c-45a0-c493-e8c7cde3b9f1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/huggingface/accelerate\n",
            "  Cloning https://github.com/huggingface/accelerate to /tmp/pip-req-build-jjzy91h2\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/accelerate /tmp/pip-req-build-jjzy91h2\n",
            "  Resolved https://github.com/huggingface/accelerate to commit e11d3ceff3a49378796cdff5b466586d877d5c60\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy<3.0.0,>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate==1.2.0.dev0) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate==1.2.0.dev0) (24.2)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate==1.2.0.dev0) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate==1.2.0.dev0) (6.0.2)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate==1.2.0.dev0) (2.5.1+cu121)\n",
            "Requirement already satisfied: huggingface_hub>=0.21.0 in /usr/local/lib/python3.10/dist-packages (from accelerate==1.2.0.dev0) (0.26.2)\n",
            "Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.10/dist-packages (from accelerate==1.2.0.dev0) (0.4.5)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub>=0.21.0->accelerate==1.2.0.dev0) (3.16.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub>=0.21.0->accelerate==1.2.0.dev0) (2024.10.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub>=0.21.0->accelerate==1.2.0.dev0) (2.32.3)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub>=0.21.0->accelerate==1.2.0.dev0) (4.66.6)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub>=0.21.0->accelerate==1.2.0.dev0) (4.12.2)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==1.2.0.dev0) (3.4.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==1.2.0.dev0) (3.1.4)\n",
            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==1.2.0.dev0) (1.13.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch>=1.10.0->accelerate==1.2.0.dev0) (1.3.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate==1.2.0.dev0) (3.0.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub>=0.21.0->accelerate==1.2.0.dev0) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub>=0.21.0->accelerate==1.2.0.dev0) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub>=0.21.0->accelerate==1.2.0.dev0) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub>=0.21.0->accelerate==1.2.0.dev0) (2024.8.30)\n",
            "Building wheels for collected packages: accelerate\n",
            "  Building wheel for accelerate (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for accelerate: filename=accelerate-1.2.0.dev0-py3-none-any.whl size=334925 sha256=fe7e7fe23693551890b431567674db7621ed125f02fa185baf8c2737226c7307\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-ex33hk33/wheels/f6/c7/9d/1b8a5ca8353d9307733bc719107acb67acdc95063bba749f26\n",
            "Successfully built accelerate\n",
            "Installing collected packages: accelerate\n",
            "  Attempting uninstall: accelerate\n",
            "    Found existing installation: accelerate 1.1.1\n",
            "    Uninstalling accelerate-1.1.1:\n",
            "      Successfully uninstalled accelerate-1.1.1\n",
            "Successfully installed accelerate-1.2.0.dev0\n"
          ]
        }
      ],
      "source": [
        "!pip install git+https://github.com/huggingface/accelerate\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -qqqU xformers"
      ],
      "metadata": {
        "id": "BFDAPw3lLh-J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8054de24-df98-45a3-fbc5-77fa71bc5ddb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/16.7 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.3/16.7 MB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:03\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.7/16.7 MB\u001b[0m \u001b[31m53.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.0/16.7 MB\u001b[0m \u001b[31m96.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m16.7/16.7 MB\u001b[0m \u001b[31m211.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m16.7/16.7 MB\u001b[0m \u001b[31m211.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.7/16.7 MB\u001b[0m \u001b[31m93.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "# Disable all integrations like WandB explicitly using the --report_to flag\n",
        "os.environ[\"TRANSFORMERS_NO_LOGGING\"] = \"true\"\n",
        "os.environ[\"WANDB_MODE\"] = \"disabled\""
      ],
      "metadata": {
        "id": "mY-Nkz3qM4AB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ANFexbEi5IWE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/480.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m \u001b[32m471.0/480.6 kB\u001b[0m \u001b[31m22.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m480.6/480.6 kB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/116.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/134.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/194.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m16.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\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",
            "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.4/44.4 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h"
          ]
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/sentence_transformers/cross_encoder/CrossEncoder.py:13: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
            "  from tqdm.autonotebook import tqdm, trange\n",
            "/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"
          ]
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            "A new version of the following files was downloaded from https://huggingface.co/dunzhang/stella_en_400M_v5:\n",
            "- configuration.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
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          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/dunzhang/stella_en_400M_v5:\n",
            "- modeling.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
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            "Some weights of the model checkpoint at dunzhang/stella_en_400M_v5 were not used when initializing NewModel: ['new.pooler.dense.bias', 'new.pooler.dense.weight']\n",
            "- This IS expected if you are initializing NewModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing NewModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n"
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              "      [  601/11150 16:07 < 4:44:01, 0.62 it/s, Epoch 0.27/5]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Cosine Accuracy@1</th>\n",
              "      <th>Cosine Accuracy@3</th>\n",
              "      <th>Cosine Accuracy@5</th>\n",
              "      <th>Cosine Accuracy@10</th>\n",
              "      <th>Cosine Precision@1</th>\n",
              "      <th>Cosine Precision@3</th>\n",
              "      <th>Cosine Precision@5</th>\n",
              "      <th>Cosine Precision@10</th>\n",
              "      <th>Cosine Recall@1</th>\n",
              "      <th>Cosine Recall@3</th>\n",
              "      <th>Cosine Recall@5</th>\n",
              "      <th>Cosine Recall@10</th>\n",
              "      <th>Cosine Ndcg@10</th>\n",
              "      <th>Cosine Mrr@10</th>\n",
              "      <th>Cosine Map@100</th>\n",
              "      <th>Dot Accuracy@1</th>\n",
              "      <th>Dot Accuracy@3</th>\n",
              "      <th>Dot Accuracy@5</th>\n",
              "      <th>Dot Accuracy@10</th>\n",
              "      <th>Dot Precision@1</th>\n",
              "      <th>Dot Precision@3</th>\n",
              "      <th>Dot Precision@5</th>\n",
              "      <th>Dot Precision@10</th>\n",
              "      <th>Dot Recall@1</th>\n",
              "      <th>Dot Recall@3</th>\n",
              "      <th>Dot Recall@5</th>\n",
              "      <th>Dot Recall@10</th>\n",
              "      <th>Dot Ndcg@10</th>\n",
              "      <th>Dot Mrr@10</th>\n",
              "      <th>Dot Map@100</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>No log</td>\n",
              "      <td>No log</td>\n",
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              "      <td>0.596126</td>\n",
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          "text": [
            "Corpus Chunks: 100%|██████████| 1/1 [00:41<00:00, 41.93s/it]\n"
          ]
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          "text": [
            "Corpus Chunks: 100%|██████████| 1/1 [00:42<00:00, 42.03s/it]\n"
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      "source": [
        "!pip install -qqq datasets\n",
        "!pip install -qqq transformers==4.45.2 sentence-transformers==3.1.1\n",
        "\n",
        "from sentence_transformers import SentenceTransformer, InputExample, losses\n",
        "from sentence_transformers.evaluation import InformationRetrievalEvaluator\n",
        "from torch.utils.data import DataLoader\n",
        "import datasets\n",
        "from huggingface_hub import login\n",
        "\n",
        "# Step 1: Log in to Hugging Face Hub\n",
        "huggingface_token = \"token\"  # Replace with your token\n",
        "login(token=huggingface_token)\n",
        "\n",
        "# Load the pre-trained model\n",
        "model_id = \"dunzhang/stella_en_400M_v5\"\n",
        "model = SentenceTransformer(model_id, trust_remote_code=True)\n",
        "\n",
        "# Load the dataset\n",
        "obliqa_dataset = datasets.load_dataset(\"DrishtiSharma/obliqa\")\n",
        "\n",
        "# Constants\n",
        "BATCH_SIZE = 10\n",
        "EPOCHS = 5\n",
        "\n",
        "# Prepare the training data\n",
        "train_dataset = obliqa_dataset['train']\n",
        "corpus = {}\n",
        "queries = {}\n",
        "relevant_docs = {}\n",
        "examples = []\n",
        "\n",
        "for row in train_dataset:\n",
        "    query_id = row['QuestionID']\n",
        "    query = row['Question']\n",
        "    passages = row['Passages']\n",
        "\n",
        "    queries[query_id] = query\n",
        "\n",
        "    for passage in passages:\n",
        "        passage_id = passage['PassageID']\n",
        "        corpus[passage_id] = passage['Passage']\n",
        "\n",
        "        if query_id not in relevant_docs:\n",
        "            relevant_docs[query_id] = []\n",
        "        relevant_docs[query_id].append(passage_id)\n",
        "\n",
        "for query_id, query in queries.items():\n",
        "    if query_id in relevant_docs and relevant_docs[query_id]:\n",
        "        passage_id = relevant_docs[query_id][0]\n",
        "        text = corpus[passage_id]\n",
        "        examples.append(InputExample(texts=[query, text]))\n",
        "\n",
        "# DataLoader and loss setup\n",
        "train_loader = DataLoader(examples, batch_size=BATCH_SIZE, shuffle=True)\n",
        "loss = losses.MultipleNegativesRankingLoss(model)\n",
        "\n",
        "# Prepare validation data\n",
        "val_dataset = obliqa_dataset['validation']\n",
        "val_corpus = {}\n",
        "val_queries = {}\n",
        "val_relevant_docs = {}\n",
        "\n",
        "for row in val_dataset:\n",
        "    query_id = row['QuestionID']\n",
        "    query = row['Question']\n",
        "    passages = row['Passages']\n",
        "\n",
        "    val_queries[query_id] = query\n",
        "\n",
        "    for passage in passages:\n",
        "        passage_id = passage['PassageID']\n",
        "        val_corpus[passage_id] = passage['Passage']\n",
        "\n",
        "        if query_id not in val_relevant_docs:\n",
        "            val_relevant_docs[query_id] = []\n",
        "        val_relevant_docs[query_id].append(passage_id)\n",
        "\n",
        "# Define evaluator\n",
        "evaluator = InformationRetrievalEvaluator(\n",
        "    queries=val_queries,\n",
        "    corpus=val_corpus,\n",
        "    relevant_docs=val_relevant_docs,\n",
        "    show_progress_bar=True\n",
        ")\n",
        "\n",
        "# Train the model\n",
        "warmup_steps = int(len(train_loader) * EPOCHS * 0.1)\n",
        "\n",
        "model.fit(\n",
        "    train_objectives=[(train_loader, loss)],\n",
        "    evaluator=evaluator,\n",
        "    epochs=EPOCHS,\n",
        "    warmup_steps=warmup_steps,\n",
        "    output_path=\"stella_en_400M_v5-obliqa-5-epochs\",\n",
        "    evaluation_steps=200,\n",
        "    show_progress_bar=True\n",
        ")\n",
        "\n",
        "print(\"Training complete.\")\n",
        "\n",
        "\n"
      ]
    },
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Fn3kdb1H5NMD"
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      "source": [
        "# Push the model to Hugging Face Hub\n",
        "model_name_on_hub = \"stella_en_400M_v5-obliqa-5-epochs\"  # Replace with your desired model name\n",
        "model.push_to_hub(model_name_on_hub)\n",
        "\n",
        "print(f\"Model has been pushed to Hugging Face Hub under the name: {model_name_on_hub}\")"
      ]
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