Test1.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EYqTSpgdtvKR",
        "outputId": "e915a825-c706-4dbb-ef4e-6e833a045bde"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "git clone https://github.com/linq-rag/FinanceRAG.git\n",
        "cd FinanceRAG"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pb1PtspftxNZ",
        "outputId": "6bbfb2de-0f5c-4578-81f7-21ff5f2e5b80"
      },
      "outputs": [],
      "source": [
        "%pip install datasets pytrec_eval nltk sentence-transformers openai"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "nGbLftv6tyjB"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "sys.path.insert(0, \"/home/azureuser/hackathon/FinanceRAG\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vmEj7h-It0pY",
        "outputId": "248faffe-a1fd-4a73-8d2c-39d7ed70b12f"
      },
      "outputs": [],
      "source": [
        "from sentence_transformers import CrossEncoder\n",
        "import logging\n",
        "\n",
        "from financerag.rerank import CrossEncoderReranker\n",
        "from financerag.retrieval import DenseRetrieval, SentenceTransformerEncoder\n",
        "from financerag.tasks import FinDER, FinQABench, FinanceBench,  ConvFinQA, FinQA, MultiHiertt, TATQA\n",
        "\n",
        "# Setup basic logging configuration to show info level messages.\n",
        "logging.basicConfig(level=logging.INFO)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "model_tag = \"intfloat/e5-large-v2\"\n",
        "\n",
        "encoder_model = None\n",
        "reranker = None\n",
        "\n",
        "if model_tag == \"dunzhang/stella_en_1.5B_v5\":\n",
        "  query_prompt = \"Instruct: Given a web search query, retrieve relevant passages that answer the query.\\nQuery: \"\n",
        "\n",
        "  encoder_model = SentenceTransformerEncoder(\n",
        "        model_name_or_path='dunzhang/stella_en_1.5B_v5',\n",
        "        # model_name_or_path='intfloat/e5-large-v2',\n",
        "        # model_name_or_path='Linq-AI-Research/Linq-Embed-Mistral',\n",
        "        query_prompt=query_prompt,\n",
        "        doc_prompt='',\n",
        "    )\n",
        "\n",
        "  reranker = CrossEncoderReranker(\n",
        "        model=CrossEncoder('cross-encoder/ms-marco-MiniLM-L-12-v2')\n",
        "    )\n",
        "elif model_tag == \"intfloat/e5-large-v2\":\n",
        "\n",
        "  encoder_model = SentenceTransformerEncoder(\n",
        "      model_name_or_path='intfloat/e5-large-v2',\n",
        "      query_prompt='query: ',\n",
        "      doc_prompt='passage: ',\n",
        "    )\n",
        "\n",
        "  reranker = CrossEncoderReranker(\n",
        "        model=CrossEncoder('cross-encoder/ms-marco-MiniLM-L-12-v2')\n",
        "    )"
      ]
    },
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        },
        "id": "pH4fkYjAt2ys",
        "outputId": "f58e71c8-83a9-403a-fffa-cbea26c30f60"
      },
      "outputs": [],
      "source": [
        "OUTPUT_FILE_NAME = \"result.csv\"\n",
        "with open(OUTPUT_FILE_NAME, 'w') as file:\n",
        "  file.write(\"query_id,corpus_id\\n\")\n",
        "\n",
        "\n",
        "\n",
        "# for task in [FinDER()]: # , FinQABench(), FinanceBench(),  ConvFinQA(), FinQA(), MultiHiertt(), TATQA()]:\n",
        "for task in [FinDER(), FinQABench(), FinanceBench(),  ConvFinQA(), FinQA(), MultiHiertt(), TATQA()]:\n",
        "  print(\"### Processing: \", task.metadata.name)\n",
        "\n",
        "  retrieval_model = DenseRetrieval(\n",
        "      model=encoder_model,\n",
        "      batch_size=256\n",
        "  )\n",
        "\n",
        "  retrieval_result = task.retrieve(\n",
        "      retriever=retrieval_model\n",
        "  )\n",
        "\n",
        "  reranking_result = task.rerank(\n",
        "      reranker=reranker,\n",
        "      results=retrieval_result,\n",
        "      top_k=100,  # Rerank the top 100 documents\n",
        "      batch_size=512\n",
        "  )\n",
        "\n",
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        "  for q_id, result in reranking_result.items():\n",
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        "\n"
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