finetune.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jasmeet0817/SelectingFood/blob/master/finetune.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup"
      ],
      "metadata": {
        "id": "Sc2rVvsOs2OP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tDCU8Sqfqaku",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c98d7624-83f7-4c2e-8101-b539847307dd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Created wheel for EbookLib: filename=EbookLib-0.18-py3-none-any.whl size=38778 sha256=8348ef123e96effd591b00f55788afe97ab0b85284212c7b8e9d9a57648430d6\n",
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            "Collecting importlib-metadata<7.0,>=6.0 (from opentelemetry-api>=1.2.0->chromadb<0.5.0,>=0.4.22->llama-index-vector-stores-chroma<0.2.0,>=0.1.1->llama-index-cli<0.2.0,>=0.1.2->llama-index)\n",
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            "\u001b[?25hCollecting opentelemetry-instrumentation-asgi==0.44b0 (from opentelemetry-instrumentation-fastapi>=0.41b0->chromadb<0.5.0,>=0.4.22->llama-index-vector-stores-chroma<0.2.0,>=0.1.1->llama-index-cli<0.2.0,>=0.1.2->llama-index)\n",
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            "Building wheels for collected packages: pypika\n",
            "  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pypika: filename=PyPika-0.48.9-py2.py3-none-any.whl size=53723 sha256=9f70bbbe1ceae5e293d291281d84c85879508a9752c312bbac08722b675a9a0d\n",
            "  Stored in directory: /root/.cache/pip/wheels/e1/26/51/d0bffb3d2fd82256676d7ad3003faea3bd6dddc9577af665f4\n",
            "Successfully built pypika\n",
            "Installing collected packages: pypika, monotonic, mmh3, dirtyjson, websockets, uvloop, python-dotenv, pypdf, PyMuPDFb, pulsar-client, overrides, orjson, opentelemetry-util-http, opentelemetry-semantic-conventions, opentelemetry-proto, mypy-extensions, marshmallow, importlib-metadata, humanfriendly, httptools, h11, deprecated, chroma-hnswlib, bcrypt, backoff, asgiref, watchfiles, uvicorn, typing-inspect, tiktoken, starlette, pymupdf, posthog, opentelemetry-exporter-otlp-proto-common, opentelemetry-api, httpcore, coloredlogs, bs4, opentelemetry-sdk, opentelemetry-instrumentation, onnxruntime, kubernetes, httpx, fastapi, dataclasses-json, opentelemetry-instrumentation-asgi, opentelemetry-exporter-otlp-proto-grpc, openai, llamaindex-py-client, opentelemetry-instrumentation-fastapi, llama-index-legacy, llama-index-core, llama-parse, llama-index-readers-file, llama-index-llms-openai, llama-index-indices-managed-llama-cloud, llama-index-embeddings-openai, chromadb, llama-index-vector-stores-chroma, llama-index-readers-llama-parse, llama-index-multi-modal-llms-openai, llama-index-agent-openai, llama-index-program-openai, llama-index-cli, llama-index-question-gen-openai, llama-index\n",
            "  Attempting uninstall: importlib-metadata\n",
            "    Found existing installation: importlib-metadata 7.0.1\n",
            "    Uninstalling importlib-metadata-7.0.1:\n",
            "      Successfully uninstalled importlib-metadata-7.0.1\n",
            "Successfully installed PyMuPDFb-1.23.22 asgiref-3.7.2 backoff-2.2.1 bcrypt-4.1.2 bs4-0.0.2 chroma-hnswlib-0.7.3 chromadb-0.4.24 coloredlogs-15.0.1 dataclasses-json-0.6.4 deprecated-1.2.14 dirtyjson-1.0.8 fastapi-0.110.0 h11-0.14.0 httpcore-1.0.4 httptools-0.6.1 httpx-0.27.0 humanfriendly-10.0 importlib-metadata-6.11.0 kubernetes-29.0.0 llama-index-0.10.14 llama-index-agent-openai-0.1.5 llama-index-cli-0.1.6 llama-index-core-0.10.14.post1 llama-index-embeddings-openai-0.1.6 llama-index-indices-managed-llama-cloud-0.1.3 llama-index-legacy-0.9.48 llama-index-llms-openai-0.1.7 llama-index-multi-modal-llms-openai-0.1.4 llama-index-program-openai-0.1.4 llama-index-question-gen-openai-0.1.3 llama-index-readers-file-0.1.6 llama-index-readers-llama-parse-0.1.3 llama-index-vector-stores-chroma-0.1.5 llama-parse-0.3.5 llamaindex-py-client-0.1.13 marshmallow-3.21.0 mmh3-4.1.0 monotonic-1.6 mypy-extensions-1.0.0 onnxruntime-1.17.1 openai-1.13.3 opentelemetry-api-1.23.0 opentelemetry-exporter-otlp-proto-common-1.23.0 opentelemetry-exporter-otlp-proto-grpc-1.23.0 opentelemetry-instrumentation-0.44b0 opentelemetry-instrumentation-asgi-0.44b0 opentelemetry-instrumentation-fastapi-0.44b0 opentelemetry-proto-1.23.0 opentelemetry-sdk-1.23.0 opentelemetry-semantic-conventions-0.44b0 opentelemetry-util-http-0.44b0 orjson-3.9.15 overrides-7.7.0 posthog-3.4.2 pulsar-client-3.4.0 pymupdf-1.23.26 pypdf-4.0.2 pypika-0.48.9 python-dotenv-1.0.1 starlette-0.36.3 tiktoken-0.6.0 typing-inspect-0.9.0 uvicorn-0.27.1 uvloop-0.19.0 watchfiles-0.21.0 websockets-12.0\n"
          ]
        }
      ],
      "source": [
        "!pip install EbookLib\n",
        "!pip install -U sentence-transformers\n",
        "!pip install llama-index --upgrade"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('data')"
      ],
      "metadata": {
        "id": "qmxFDe9TqhNu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "acd0c6e9-ee38-42b5-e107-1fa3f2f8b297"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "DATA_FOLDER = '/content/data/MyDrive/Colab Notebooks/book-llm/data/'"
      ],
      "metadata": {
        "id": "EuwWt2psrTFQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wbq8PueWqakx"
      },
      "source": [
        "## Generate Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xIibgXHAqakz",
        "outputId": "e2d5475d-d881-4b54-c0c3-b3d6142881b0"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/jesse/.conda/envs/llm_pip/lib/python3.12/site-packages/ebooklib/epub.py:1395: UserWarning: In the future version we will turn default option ignore_ncx to True.\n",
            "  warnings.warn('In the future version we will turn default option ignore_ncx to True.')\n"
          ]
        }
      ],
      "source": [
        "from bs4 import BeautifulSoup\n",
        "from ebooklib import epub\n",
        "from llama_index.core.node_parser import SimpleNodeParser\n",
        "from llama_index.core import Document\n",
        "from llama_index.core.schema import MetadataMode\n",
        "\n",
        "\n",
        "def html_to_text(html):\n",
        "    soup = BeautifulSoup(html, \"html.parser\")\n",
        "    return soup.get_text()\n",
        "\n",
        "def get_book_content(file_path, search_str, chunk_size, chunk_overlap):\n",
        "    book = epub.read_epub(file_path)\n",
        "    documents = []\n",
        "    for item in book.get_items_of_type(ebooklib.ITEM_DOCUMENT):\n",
        "        content = html_to_text(item.get_content().decode('utf-8')).strip()\n",
        "        if content == '':\n",
        "            continue\n",
        "        documents.append(Document(text=content))\n",
        "        if search_str is not None and search_str in content:\n",
        "            break\n",
        "\n",
        "    # parser = SentenceSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)\n",
        "    nodes = SimpleNodeParser.from_defaults().get_nodes_from_documents(documents)\n",
        "    corpus = {node.node_id: node.get_content(metadata_mode=MetadataMode.NONE) for node in nodes}\n",
        "    return corpus\n",
        "\n",
        "training_corpus = get_book_content(DATA_FOLDER + 'training_data.epub', None, 512, 50)\n",
        "val_corpus = get_book_content(DATA_FOLDER + 'validation_data.epub', None, 512, 50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Iiu70Qzrqak1"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "\n",
        "TRAIN_CORPUS_FPATH = DATA_FOLDER + 'train_corpus.json'\n",
        "VAL_CORPUS_FPATH = DATA_FOLDER + 'val_corpus.json'\n",
        "\n",
        "with open(TRAIN_CORPUS_FPATH, 'w+') as f:\n",
        "    json.dump(training_corpus, f)\n",
        "\n",
        "with open(VAL_CORPUS_FPATH, 'w+') as f:\n",
        "    json.dump(val_corpus, f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CoYGbbxsqak1"
      },
      "outputs": [],
      "source": [
        "import openai\n",
        "\n",
        "OPENAI_KEY = \"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\"\n",
        "client = openai.OpenAI(\n",
        "    # This is the default and can be omitted\n",
        "    api_key=OPENAI_KEY,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "knzRQNDnqak2"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "import uuid\n",
        "from tqdm import tqdm\n",
        "\n",
        "\n",
        "def generate_queries(\n",
        "    corpus,\n",
        "    num_questions_per_chunk=2,\n",
        "    prompt_template=None,\n",
        "):\n",
        "    \"\"\"\n",
        "    Automatically generate hypothetical questions that could be answered with\n",
        "    doc in the corpus.\n",
        "    \"\"\"\n",
        "\n",
        "    prompt_template = prompt_template or \"\"\"\\\n",
        "    Context information is below.\n",
        "\n",
        "    ---------------------\n",
        "    {context_str}\n",
        "    ---------------------\n",
        "\n",
        "    Given the context information and not prior knowledge.\n",
        "    generate only questions based on the below query.\n",
        "\n",
        "    You are a Teacher/ Professor. Your task is to setup \\\n",
        "    {num_questions_per_chunk} questions for an upcoming \\\n",
        "    quiz/examination. The questions should be diverse in nature \\\n",
        "    across the document. Restrict the questions to the \\\n",
        "    context information provided.\"\n",
        "    \"\"\"\n",
        "\n",
        "    queries = {}\n",
        "    relevant_docs = {}\n",
        "    for node_id, text in tqdm(corpus.items()):\n",
        "        query = prompt_template.format(context_str=text, num_questions_per_chunk=num_questions_per_chunk)\n",
        "        response = client.chat.completions.create(\n",
        "            model=\"gpt-3.5-turbo-0125\",\n",
        "            messages=[\n",
        "                {\"role\": \"user\", \"content\": query},\n",
        "            ]\n",
        "        )\n",
        "        result = str(response).strip().split(\"\\n\")\n",
        "        questions = [\n",
        "            re.sub(r\"^\\d+[\\).\\s]\", \"\", question).strip() for question in result\n",
        "        ]\n",
        "        questions = [question for question in questions if len(question) > 0]\n",
        "\n",
        "        for question in questions:\n",
        "            question_id = str(uuid.uuid4())\n",
        "            queries[question_id] = question\n",
        "            relevant_docs[question_id] = [node_id]\n",
        "    return queries, relevant_docs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3x22N4hQqak2",
        "outputId": "4296c67e-c5c7-418b-82b6-0611b7bf2148"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 174/174 [08:11<00:00,  2.82s/it]\n"
          ]
        }
      ],
      "source": [
        "train_queries, train_relevant_docs = generate_queries(training_corpus, num_questions_per_chunk=3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "091MOEUyqak3",
        "outputId": "37f94d6c-aa49-42a9-d8bd-173a83d6113a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 60/60 [03:12<00:00,  3.21s/it]\n"
          ]
        }
      ],
      "source": [
        "val_queries, val_relevant_docs = generate_queries(val_corpus, num_questions_per_chunk=3)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I-htzEXDqak4"
      },
      "outputs": [],
      "source": [
        "TRAIN_QUERIES_FPATH = DATA_FOLDER + 'train_queries.json'\n",
        "TRAIN_RELEVANT_DOCS_FPATH = DATA_FOLDER + 'train_relevant_docs.json'\n",
        "\n",
        "VAL_QUERIES_FPATH = DATA_FOLDER + 'val_queries.json'\n",
        "VAL_RELEVANT_DOCS_FPATH = DATA_FOLDER + 'val_relevant_docs.json'\n",
        "\n",
        "with open(TRAIN_QUERIES_FPATH, 'w+') as f:\n",
        "    json.dump(train_queries, f)\n",
        "\n",
        "with open(TRAIN_RELEVANT_DOCS_FPATH, 'w+') as f:\n",
        "    json.dump(train_relevant_docs, f)\n",
        "\n",
        "with open(VAL_QUERIES_FPATH, 'w+') as f:\n",
        "    json.dump(val_queries, f)\n",
        "\n",
        "with open(VAL_RELEVANT_DOCS_FPATH, 'w+') as f:\n",
        "    json.dump(val_relevant_docs, f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Th3qfUj4qak4"
      },
      "outputs": [],
      "source": [
        "TRAIN_DATASET_FPATH = DATA_FOLDER + 'train_dataset.json'\n",
        "VAL_DATASET_FPATH = DATA_FOLDER + 'val_dataset.json'\n",
        "\n",
        "train_dataset = {\n",
        "    'queries': train_queries,\n",
        "    'corpus': training_corpus,\n",
        "    'relevant_docs': train_relevant_docs,\n",
        "}\n",
        "\n",
        "val_dataset = {\n",
        "    'queries': val_queries,\n",
        "    'corpus': val_corpus,\n",
        "    'relevant_docs': val_relevant_docs,\n",
        "}\n",
        "\n",
        "\n",
        "with open(TRAIN_DATASET_FPATH, 'w+') as f:\n",
        "    json.dump(train_dataset, f)\n",
        "\n",
        "with open(VAL_DATASET_FPATH, 'w+') as f:\n",
        "    json.dump(val_dataset, f)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B9Ajx11cqak4"
      },
      "source": [
        "## Finetune"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gRmXeggAqak5"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "\n",
        "TRAIN_DATASET_FPATH = DATA_FOLDER + 'train_dataset.json'\n",
        "VAL_DATASET_FPATH = DATA_FOLDER + 'val_dataset.json'\n",
        "\n",
        "\n",
        "with open(TRAIN_DATASET_FPATH, 'r+') as f:\n",
        "    train_dataset = json.load(f)\n",
        "\n",
        "with open(VAL_DATASET_FPATH, 'r+') as f:\n",
        "    val_dataset = json.load(f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DbJ6jQ5Eqak5"
      },
      "outputs": [],
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "model_id = \"BAAI/bge-base-en-v1.5\"\n",
        "model = SentenceTransformer(model_id)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r-B_w-5sqak5"
      },
      "outputs": [],
      "source": [
        "from sentence_transformers import InputExample\n",
        "\n",
        "dataset = train_dataset\n",
        "\n",
        "corpus = dataset['corpus']\n",
        "queries = dataset['queries']\n",
        "relevant_docs = dataset['relevant_docs']\n",
        "\n",
        "examples = []\n",
        "for query_id, query in queries.items():\n",
        "    node_id = relevant_docs[query_id][0]\n",
        "    text = corpus[node_id]\n",
        "    example = InputExample(texts=[query, text])\n",
        "    examples.append(example)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1hLvEJq4qak5"
      },
      "outputs": [],
      "source": [
        "BATCH_SIZE = 32\n",
        "\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "loader = DataLoader(\n",
        "    examples, batch_size=BATCH_SIZE\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wei1CVA8qak5"
      },
      "source": [
        "### Define Loss\n",
        "\n",
        "\n",
        "MultipleNegativesRankingLoss is a great loss function if you only have positive pairs, for example, only pairs of similar texts like pairs of paraphrases, pairs of duplicate questions, pairs of (query, response), or pairs of (source_language, target_language).\n",
        "\n",
        "This loss function works great to train embeddings for retrieval setups where you have positive pairs (e.g. (query, relevant_doc)) as it will sample in each batch n-1 negative docs randomly.\n",
        "\n",
        "The performance usually increases with increasing batch sizes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Wv0AgowXqak6"
      },
      "outputs": [],
      "source": [
        "from sentence_transformers import losses\n",
        "\n",
        "loss = losses.MultipleNegativesRankingLoss(model)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rq3bdE1bqak6"
      },
      "source": [
        "### Evaluator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rv5vJLOSqak6"
      },
      "outputs": [],
      "source": [
        "from sentence_transformers.evaluation import InformationRetrievalEvaluator\n",
        "\n",
        "dataset = val_dataset\n",
        "\n",
        "corpus = dataset['corpus']\n",
        "queries = dataset['queries']\n",
        "relevant_docs = dataset['relevant_docs']\n",
        "\n",
        "evaluator = InformationRetrievalEvaluator(queries, corpus, relevant_docs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "srwgN1Tmqak6"
      },
      "source": [
        "### Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FLyKTwqqqak6"
      },
      "outputs": [],
      "source": [
        "EPOCHS = 20"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7ogcQUG4qak6",
        "outputId": "ad785984-29e1-4570-912c-a45651ade7cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689,
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      "outputs": [
        {
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            "text/plain": [
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              "model_id": "2a5327ca4e634d1ab04add9c3e1c0c0e"
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          "metadata": {}
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        {
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          "data": {
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            ],
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          },
          "metadata": {}
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      ],
      "source": [
        "warmup_steps = int(len(loader) * EPOCHS * 0.1)\n",
        "\n",
        "model.fit(\n",
        "    train_objectives=[(loader, loss)],\n",
        "    epochs=EPOCHS,\n",
        "    warmup_steps=warmup_steps,\n",
        "    output_path='exp_finetune',\n",
        "    show_progress_bar=True,\n",
        "    evaluator=evaluator,\n",
        "    evaluation_steps=50,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_save_path = DATA_FOLDER + 'finetuned_bge_base'\n",
        "model.save(model_save_path)"
      ],
      "metadata": {
        "id": "lBsPyzyPLSIm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Evaluation"
      ],
      "metadata": {
        "id": "2A9Pn4igKrP3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Setup"
      ],
      "metadata": {
        "id": "Lng13CaW_oHl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "TRAIN_DATASET_FPATH = DATA_FOLDER + 'train_dataset.json'\n",
        "VAL_DATASET_FPATH = DATA_FOLDER + 'val_dataset.json'\n",
        "\n",
        "\n",
        "with open(TRAIN_DATASET_FPATH, 'r+') as f:\n",
        "    train_dataset = json.load(f)\n",
        "\n",
        "with open(VAL_DATASET_FPATH, 'r+') as f:\n",
        "    val_dataset = json.load(f)"
      ],
      "metadata": {
        "id": "oKH38FCYKvbI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Define eval function\n",
        "We use the InformationRetrievalEvaluator from sentence_transformers.\n",
        "\n",
        "This provides a more comprehensive suite of metrics, but we can only run it against the sentencetransformers compatible models (open source and our finetuned model, not the OpenAI embedding model)."
      ],
      "metadata": {
        "id": "TMsN5pzwLC0w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers.evaluation import InformationRetrievalEvaluator\n",
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "def evaluate(\n",
        "    dataset,\n",
        "    model_id,\n",
        "    name,\n",
        "):\n",
        "    corpus = dataset['corpus']\n",
        "    queries = dataset['queries']\n",
        "    relevant_docs = dataset['relevant_docs']\n",
        "\n",
        "    evaluator = InformationRetrievalEvaluator(queries, corpus, relevant_docs, name=name)\n",
        "    model = SentenceTransformer(model_id)\n",
        "    return evaluator(model, output_path=DATA_FOLDER)"
      ],
      "metadata": {
        "id": "czhGcL-iLNie"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Run evals"
      ],
      "metadata": {
        "id": "HXYiAwKlLcSi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### BGE Small"
      ],
      "metadata": {
        "id": "5xUPy_i0LwCo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bge_val_results = evaluate(val_dataset, 'BAAI/bge-small-en', name='bge-small')\n",
        "bge_val_results"
      ],
      "metadata": {
        "id": "IGdB_7Dfzf4A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bge_val_results"
      ],
      "metadata": {
        "id": "mQIGi1tSL2-x",
        "colab": {
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        },
        "outputId": "dcf7f5f3-3de7-4a24-8eac-49f765b16ced"
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      "execution_count": null,
      "outputs": [
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          "metadata": {},
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      "source": [
        "#### BGE Base"
      ],
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        "id": "yLqCNz289EyO"
      }
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      "source": [
        "evaluate(val_dataset, 'BAAI/bge-large-en-v1.5', name='bge-large')"
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        {
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          "metadata": {},
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    {
      "cell_type": "markdown",
      "source": [
        "#### BGE Large"
      ],
      "metadata": {
        "id": "iVyyP5vY-_1g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate(val_dataset, 'BAAI/bge-base-en-v1.5', name='bge-base')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d53db38-af9f-4436-8e27-786a2f962a93",
        "id": "osecW9gP-_1v"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.578128306878307"
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          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Finetuned BGE Small"
      ],
      "metadata": {
        "id": "6HruXk4RMAOD"
      }
    },
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      "cell_type": "code",
      "source": [
        "val_results_finetuned = evaluate(val_dataset, DATA_FOLDER + 'finetuned_bge_small', name='finetuned_bge_small')\n",
        "val_results_finetuned"
      ],
      "metadata": {
        "id": "0sHr_sS7MAOM"
      },
      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "val_results_finetuned"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "9d43579b-7dcd-4773-8e78-5aa1f9a9c1db"
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      "execution_count": null,
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          "metadata": {},
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    {
      "cell_type": "markdown",
      "source": [
        "#### Finetuned BGE Base"
      ],
      "metadata": {
        "id": "eX1fjnEb9fdV"
      }
    },
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      "source": [
        "evaluate(val_dataset, DATA_FOLDER + 'finetuned_bge_base', name='finetuned_bge_base')"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "vnaggZR89fdp",
        "outputId": "817f1622-b002-468d-9c7e-1582fb704b9d"
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          },
          "metadata": {},
          "execution_count": 16
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    {
      "cell_type": "markdown",
      "source": [
        "#### Finetuned BGE Large"
      ],
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        "id": "DN4GT2JnAJRJ"
      }
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      "source": [
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      "execution_count": null,
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          },
          "metadata": {},
          "execution_count": 19
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