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"grid_row": null, "max_height": null, "align_content": null, "visibility": null, "align_self": null, "height": null, "min_height": null, "padding": null, "grid_auto_rows": null, "grid_gap": null, "max_width": null, "order": null, "_view_module_version": "1.2.0", "grid_template_areas": null, "object_position": null, "object_fit": null, "grid_auto_columns": null, "margin": null, "display": null, "left": null } } } }, "pycharm": { "stem_cell": { "cell_type": "raw", "source": [], "metadata": { "collapsed": false } } } }, "cells": [ { "cell_type": "markdown", "source": [ "# Evaluation of a QA System\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial5_Evaluation.ipynb)\n", "\n", "To be able to make a statement about the performance of a question-answering system, it is important to evalute it. Furthermore, evaluation allows to determine which parts of the system can be improved." ], "metadata": { "collapsed": true, "id": "MGSXn0USOhtu", "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "markdown", "source": [ "### Prepare environment\n", "\n", "#### Colab: Enable the GPU runtime\n", "Make sure you enable the GPU runtime to experience decent speed in this tutorial.\n", "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n", "\n", "" ], "metadata": { "collapsed": false, "id": "lEKOjCS5U7so" } }, { "cell_type": "code", "execution_count": 1, "source": [ "# Make sure you have a GPU running\n", "!nvidia-smi" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mon Oct 25 12:02:00 2021 \n", "+-----------------------------------------------------------------------------+\n", "| NVIDIA-SMI 470.74 Driver Version: 460.32.03 CUDA Version: 11.2 |\n", "|-------------------------------+----------------------+----------------------+\n", "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", "| | | MIG M. |\n", "|===============================+======================+======================|\n", "| 0 Tesla K80 Off | 00000000:00:04.0 Off | 0 |\n", "| N/A 35C P8 27W / 149W | 0MiB / 11441MiB | 0% Default |\n", "| | | N/A |\n", "+-------------------------------+----------------------+----------------------+\n", " \n", "+-----------------------------------------------------------------------------+\n", "| Processes: |\n", "| GPU GI CI PID Type Process name GPU Memory |\n", "| ID ID Usage |\n", "|=============================================================================|\n", "| No running processes found |\n", "+-----------------------------------------------------------------------------+\n" ] } ], "metadata": { "pycharm": { "name": "#%%\n" }, "id": "xhFIMX_7U7ss", "outputId": "285b2491-01e5-4bfd-cba9-c2279d4417c4", "colab": { "base_uri": "https://localhost:8080/" } } }, { "cell_type": "markdown", "source": [ "## Start an Elasticsearch server\n", "You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source." ], "metadata": { "id": "E6H_7lAmOht8" } }, { "cell_type": "code", "execution_count": 2, "source": [ "# Install the latest release of Haystack in your own environment \n", "#! pip install farm-haystack\n", "\n", "# Install the latest master of Haystack\n", "!pip install grpcio-tools==1.34.1\n", "!pip install git+https://github.com/deepset-ai/haystack.git\n", "\n", "# If you run this notebook on Google Colab, you might need to\n", "# restart the runtime after installing haystack." ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting grpcio-tools==1.34.1\n", " Downloading grpcio_tools-1.34.1-cp37-cp37m-manylinux2014_x86_64.whl (2.5 MB)\n", "\u001b[K |████████████████████████████████| 2.5 MB 12.2 MB/s \n", "\u001b[?25hRequirement already satisfied: protobuf<4.0dev,>=3.5.0.post1 in 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gitpython, docker, databricks-cli, colorama, azure-storage-blob, asgiref, alembic, aioredis, aiohttp-cors, weaviate-client, uvloop, uvicorn, tox, tika, sqlalchemy-utils, SPARQLWrapper, seqeval, sentence-transformers, ray, quantulum3, python-multipart, python-docx, pytesseract, pymilvus, psycopg2-binary, pdf2image, mmh3, mlflow, langdetect, httptools, fastapi, faiss-cpu, elasticsearch, elastic-apm, dataclasses-json, farm-haystack\n", " Attempting uninstall: pillow\n", " Found existing installation: Pillow 7.1.2\n", " Uninstalling Pillow-7.1.2:\n", " Successfully uninstalled Pillow-7.1.2\n", " Attempting uninstall: grpcio\n", " Found existing installation: grpcio 1.41.0\n", " Uninstalling grpcio-1.41.0:\n", " Successfully uninstalled grpcio-1.41.0\n", " Attempting uninstall: pluggy\n", " Found existing installation: pluggy 0.7.1\n", " Uninstalling pluggy-0.7.1:\n", " Successfully uninstalled pluggy-0.7.1\n", "\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", "pytest 3.6.4 requires pluggy<0.8,>=0.5, but you have pluggy 1.0.0 which is incompatible.\n", "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n", "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n", "Successfully installed Mako-1.1.5 SPARQLWrapper-1.8.5 aiohttp-3.7.4.post0 aiohttp-cors-0.7.0 aioredis-2.0.0 alembic-1.4.1 anyio-3.3.4 asgiref-3.4.1 async-timeout-3.0.1 azure-core-1.19.0 azure-storage-blob-12.9.0 backports.entry-points-selectable-1.1.0 blessings-1.7 colorama-0.4.4 cryptography-35.0.0 databricks-cli-0.16.2 dataclasses-json-0.5.6 distlib-0.3.3 docker-5.0.3 elastic-apm-6.6.0 elasticsearch-7.10.0 faiss-cpu-1.7.1.post2 farm-haystack-1.0.0rc1 fastapi-0.70.0 gitdb-4.0.9 gitpython-3.1.24 gpustat-0.6.0 grpcio-1.37.1 gunicorn-20.1.0 h11-0.12.0 httptools-0.3.0 huggingface-hub-0.0.8 isodate-0.6.0 langdetect-1.0.9 marshmallow-3.14.0 marshmallow-enum-1.5.1 mlflow-1.13.1 mmh3-3.0.0 msrest-0.6.21 multidict-5.2.0 mypy-extensions-0.4.3 num2words-0.5.10 opencensus-0.8.0 opencensus-context-0.1.2 pdf2image-1.14.0 pillow-8.3.2 platformdirs-2.4.0 pluggy-1.0.0 prometheus-flask-exporter-0.18.4 psycopg2-binary-2.9.1 py-spy-0.3.10 pydantic-1.8.2 pymilvus-1.1.2 pytesseract-0.3.7 python-docx-0.8.11 python-editor-1.0.4 python-multipart-0.0.5 quantulum3-0.7.9 querystring-parser-1.2.4 ray-1.5.0 rdflib-6.0.2 redis-3.5.3 sacremoses-0.0.46 sentence-transformers-2.1.0 sentencepiece-0.1.96 seqeval-1.2.2 smmap-5.0.0 sniffio-1.2.0 sqlalchemy-utils-0.37.9 starlette-0.16.0 tika-1.24 tokenizers-0.10.3 tox-3.24.4 transformers-4.7.0 typing-inspect-0.7.1 ujson-4.2.0 uvicorn-0.15.0 uvloop-0.14.0 validators-0.18.2 virtualenv-20.9.0 weaviate-client-2.5.0 websocket-client-1.2.1 yarl-1.7.0\n" ] }, { "output_type": "display_data", "data": { "application/vnd.colab-display-data+json": { "pip_warning": { "packages": [ "PIL" ] } } }, "metadata": {} } ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "vgmFOp82Oht_", "pycharm": { "name": "#%%\n" }, "outputId": "5bbcbb42-3a90-43a9-ebfd-598a98fa7143" } }, { "cell_type": "code", "execution_count": 3, "source": [ "# In Colab / No Docker environments: Start Elasticsearch from source\n", "! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q\n", "! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz\n", "! chown -R daemon:daemon elasticsearch-7.9.2\n", "\n", "import os\n", "from subprocess import Popen, PIPE, STDOUT\n", "es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],\n", " stdout=PIPE, stderr=STDOUT,\n", " preexec_fn=lambda: os.setuid(1) # as daemon\n", " )\n", "# wait until ES has started\n", "! sleep 30" ], "outputs": [], "metadata": { "id": "tNoaWcDKOhuL", "pycharm": { "is_executing": true, "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 1, "source": [ "from haystack.modeling.utils import initialize_device_settings\n", "\n", "device, n_gpu = initialize_device_settings(use_cuda=True)" ], "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/ray/autoscaler/_private/cli_logger.py:61: FutureWarning: Not all Ray CLI dependencies were found. In Ray 1.4+, the Ray CLI, autoscaler, and dashboard will only be usable via `pip install 'ray[default]'`. Please update your install command.\n", " \"update your install command.\", FutureWarning)\n" ] } ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "w0MHgxrYOhur", "pycharm": { "name": "#%%\n" }, "outputId": "b1db4179-b278-4e42-f326-509292b94fbd" } }, { "cell_type": "code", "execution_count": 2, "source": [ "from haystack.utils import fetch_archive_from_http\n", "\n", "# Download evaluation data, which is a subset of Natural Questions development set containing 50 documents\n", "doc_dir = \"../data/nq\"\n", "s3_url = \"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/nq_dev_subset_v2.json.zip\"\n", "fetch_archive_from_http(url=s3_url, output_dir=doc_dir)" ], "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "True" ] }, "metadata": {}, "execution_count": 2 } ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "tTXxr6TAOhuz", "pycharm": { "name": "#%%\n" }, "outputId": "586d4775-4354-4ed9-a72c-c30bedcdfbee" } }, { "cell_type": "code", "execution_count": 3, "source": [ "# make sure these indices do not collide with existing ones, the indices will be wiped clean before data is inserted\n", "doc_index = \"tutorial5_docs\"\n", "label_index = \"tutorial5_labels\"" ], "outputs": [], "metadata": { "id": "T-G7Ay2jU7s_" } }, { "cell_type": "code", "execution_count": 4, "source": [ "# Connect to Elasticsearch\n", "from haystack.document_stores import ElasticsearchDocumentStore\n", "\n", "# Connect to Elasticsearch\n", "document_store = ElasticsearchDocumentStore(host=\"localhost\", username=\"\", password=\"\", index=\"document\",\n", " create_index=False, embedding_field=\"emb\",\n", " embedding_dim=768, excluded_meta_data=[\"emb\"])" ], "outputs": [], "metadata": { "id": "B_NEtezLOhu5", "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 5, "source": [ "from haystack.nodes import PreProcessor\n", "\n", "# Add evaluation data to Elasticsearch Document Store\n", "# We first delete the custom tutorial indices to not have duplicate elements\n", "# and also split our documents into shorter passages using the PreProcessor\n", "preprocessor = PreProcessor(\n", " split_length=200,\n", " split_overlap=0,\n", " split_respect_sentence_boundary=False,\n", " clean_empty_lines=False,\n", " clean_whitespace=False\n", ")\n", "document_store.delete_documents(index=doc_index)\n", "document_store.delete_documents(index=label_index)\n", "document_store.add_eval_data(\n", " filename=\"../data/nq/nq_dev_subset_v2.json\",\n", " doc_index=doc_index,\n", " label_index=label_index,\n", " preprocessor=preprocessor\n", ")\n", "\n", "# Let's prepare the labels that we need for the retriever and the reader\n", "labels = document_store.get_all_labels_aggregated(index=label_index, drop_negative_labels=True, drop_no_answers=False)" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[nltk_data] Downloading package punkt to /root/nltk_data...\n", "[nltk_data] Unzipping tokenizers/punkt.zip.\n" ] } ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bRFsQUAJOhu_", "pycharm": { "name": "#%%\n" }, "outputId": "477031b9-5c2c-4128-ef5f-54db86259734" } }, { "cell_type": "markdown", "source": [ "## Initialize components of QA-System" ], "metadata": { "id": "gy8YwmSYOhvE", "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": 6, "source": [ "# Initialize Retriever\n", "from haystack.nodes import ElasticsearchRetriever\n", "retriever = ElasticsearchRetriever(document_store=document_store)\n", "# Alternative: Evaluate dense Retrievers (DPR and SentenceTransformers)\n", "# Dense Passage Retrieval uses a separate transformer based encoder for query and document each\n", "# SentenceTransformers have a single encoder for both\n", "# Please make sure the \"embedding_dim\" parameter in the DocumentStore above matches the output dimension of you models!\n", "# Please also take care that the PreProcessor splits your files into chunks that can be completely converted with\n", "# the max_seq_len limitations of Transformers\n", "# The SentenceTransformer model \"all-mpnet-base-v2\" generelly works well on any kind of english text.\n", "# For more information check out the documentation at: https://www.sbert.net/docs/pretrained_models.html\n", "# from haystack.retriever import DensePassageRetriever, EmbeddingRetriever\n", "# retriever = DensePassageRetriever(document_store=document_store,\n", "# query_embedding_model=\"facebook/dpr-question_encoder-single-nq-base\",\n", "# passage_embedding_model=\"facebook/dpr-ctx_encoder-single-nq-base\",\n", "# use_gpu=True,\n", "# max_seq_len_passage=256,\n", "# embed_title=True)\n", "# retriever = EmbeddingRetriever(document_store=document_store, model_format=\"sentence_transformers\",\n", "# embedding_model=\"all-mpnet-base-v2\")\n", "# document_store.update_embeddings(retriever, index=doc_index)" ], "outputs": [], "metadata": { "id": "JkhaPMIJOhvF", "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 7, "source": [ "# Initialize Reader\n", "from haystack.nodes import FARMReader\n", "\n", "reader = FARMReader(\"deepset/roberta-base-squad2\", top_k=4, return_no_answer=True)\n" ], "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "118aedffeecd4f109ae04a4561baeb08", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/571 [00:00