mirror of
https://github.com/deepset-ai/haystack.git
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1964 lines
58 KiB
Plaintext
1964 lines
58 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"collapsed": true,
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"id": "MGSXn0USOhtu",
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"# Evaluation of a QA System\n",
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"\n",
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"EXECUTABLE VERSION: [colab](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial5_Evaluation.ipynb)\n",
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"\n",
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"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."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "E6H_7lAmOht8"
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},
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"source": [
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"## Start an Elasticsearch server\n",
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "vgmFOp82Oht_",
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# Install the latest release of Haystack in your own environment \n",
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"#! pip install farm-haystack\n",
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"\n",
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"# Install the latest master of Haystack\n",
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"!pip install git+https://github.com/deepset-ai/haystack.git\n",
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"!pip install urllib3==1.25.4\n",
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"!pip install torch==1.6.0+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "tNoaWcDKOhuL",
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"pycharm": {
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"is_executing": true,
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# In Colab / No Docker environments: Start Elasticsearch from source\n",
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"! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q\n",
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"! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz\n",
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"! chown -R daemon:daemon elasticsearch-7.9.2\n",
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"\n",
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"import os\n",
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"from subprocess import Popen, PIPE, STDOUT\n",
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"es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],\n",
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" stdout=PIPE, stderr=STDOUT,\n",
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" preexec_fn=lambda: os.setuid(1) # as daemon\n",
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" )\n",
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"# wait until ES has started\n",
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"! sleep 30"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 54
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},
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"colab_type": "code",
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"id": "w0MHgxrYOhur",
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"outputId": "9e530bf3-44b1-4ea1-86e2-8be0bb9163ad",
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"from farm.utils import initialize_device_settings\n",
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"\n",
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"device, n_gpu = initialize_device_settings(use_cuda=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 87
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},
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"colab_type": "code",
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"id": "tTXxr6TAOhuz",
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"outputId": "99a4e32b-e0ec-4c94-dab3-1a09c53d4dc1",
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"from haystack.preprocessor.utils import fetch_archive_from_http\n",
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"\n",
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"# Download evaluation data, which is a subset of Natural Questions development set containing 50 documents\n",
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"doc_dir = \"../data/nq\"\n",
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"s3_url = \"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/nq_dev_subset_v2.json.zip\"\n",
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"fetch_archive_from_http(url=s3_url, output_dir=doc_dir)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# make sure these indices do not collide with existing ones, the indices will be wiped clean before data is inserted\n",
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"doc_index = \"tutorial5_docs\"\n",
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"label_index = \"tutorial5_labels\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "B_NEtezLOhu5",
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# Connect to Elasticsearch\n",
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"from haystack.document_store.elasticsearch import ElasticsearchDocumentStore\n",
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"\n",
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"# Connect to Elasticsearch\n",
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"document_store = ElasticsearchDocumentStore(host=\"localhost\", username=\"\", password=\"\", index=\"document\",\n",
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" create_index=False, embedding_field=\"emb\",\n",
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" embedding_dim=768, excluded_meta_data=[\"emb\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 71
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},
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"colab_type": "code",
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"id": "bRFsQUAJOhu_",
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"outputId": "56b84800-c524-4418-9664-e2720b66a1af",
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"pycharm": {
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"is_executing": true,
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# Add evaluation data to Elasticsearch Document Store\n",
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"# We first delete the custom tutorial indices to not have duplicate elements\n",
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"document_store.delete_all_documents(index=doc_index)\n",
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"document_store.delete_all_documents(index=label_index)\n",
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"document_store.add_eval_data(filename=\"../data/nq/nq_dev_subset_v2.json\", doc_index=doc_index, label_index=label_index)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "gy8YwmSYOhvE",
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"## Initialize components of QA-System"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "JkhaPMIJOhvF",
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# Initialize Retriever\n",
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"from haystack.retriever.sparse import ElasticsearchRetriever\n",
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"retriever = ElasticsearchRetriever(document_store=document_store)\n",
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"# Alternative: Evaluate DensePassageRetriever\n",
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"# Note, that DPR works best when you index short passages < 512 tokens as only those tokens will be used for the embedding.\n",
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"# Here, for nq_dev_subset_v2.json we have avg. num of tokens = 5220(!).\n",
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"# DPR still outperforms Elastic's BM25 by a small margin here.\n",
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"# from haystack.retriever.dense import DensePassageRetriever\n",
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"# retriever = DensePassageRetriever(document_store=document_store,\n",
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"# query_embedding_model=\"facebook/dpr-question_encoder-single-nq-base\",\n",
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"# passage_embedding_model=\"facebook/dpr-ctx_encoder-single-nq-base\",\n",
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"# use_gpu=True,\n",
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"# embed_title=True,\n",
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"# max_seq_len=256,\n",
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"# batch_size=16,\n",
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"# remove_sep_tok_from_untitled_passages=True)\n",
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"#document_store.update_embeddings(retriever, index=doc_index)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 725,
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"referenced_widgets": [
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"398e8dc496594a5f8e65daecc3ddad4a",
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"ee436c1e0fb24802b5d0706299ce7e81",
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"479f015ad2e8412d96a34ddb59a054d1",
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"ef088eda6fe04abe958059620da9b5d0",
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"e1819597e4d840b19cfa8aba00ef3ef1",
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"19173b1d00314580a4f40efa0df4b174",
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"d7ce404f2f784b0f93abf8f3ba592e18",
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"9da1585dcdd440fa8b2e8351ffeba348",
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"8880e647ca4a4796b571f5b983b541b4",
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"01d9cd6656494aef83a3947945c0acdd",
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"b01601a11a324141b2d88190a87ef950",
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"c7db7b055dbe4361965ca4ab8c028536",
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"c1852f69951e4d80aecb5a6c942b2ca6"
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]
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},
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"colab_type": "code",
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"id": "cW3Ypn_gOhvK",
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"outputId": "89ad5598-1017-499f-c986-72bba2a3a6cb",
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"pycharm": {
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"is_executing": true,
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# Initialize Reader\n",
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"from haystack.reader.farm import FARMReader\n",
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"\n",
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"reader = FARMReader(\"deepset/roberta-base-squad2\", top_k_per_candidate=4)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "gOs7qy4xOhvO",
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"pycharm": {
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"is_executing": true,
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# Initialize Finder which sticks together Reader and Retriever\n",
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"from haystack.finder import Finder\n",
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"\n",
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"finder = Finder(reader, retriever)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "qwkBgzh5OhvR",
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"## Evaluation of Retriever"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 1000
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},
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"colab_type": "code",
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"id": "YzvLhnx3OhvS",
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"outputId": "1d45f072-0ae0-4864-8ccc-aa12303a8d04",
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"pycharm": {
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"is_executing": true,
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"## Evaluate Retriever on its own\n",
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"retriever_eval_results = retriever.eval(top_k=20, label_index=label_index, doc_index=doc_index)\n",
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"## Retriever Recall is the proportion of questions for which the correct document containing the answer is\n",
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"## among the correct documents\n",
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"print(\"Retriever Recall:\", retriever_eval_results[\"recall\"])\n",
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"## Retriever Mean Avg Precision rewards retrievers that give relevant documents a higher rank\n",
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"print(\"Retriever Mean Avg Precision:\", retriever_eval_results[\"map\"])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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||
"id": "fjZRnB6bOhvW",
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"## Evaluation of Reader"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 203
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},
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"colab_type": "code",
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"id": "Lgsgf4KaOhvY",
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"outputId": "24d3755e-bf2e-4396-f1a2-59c925cc54d3",
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"pycharm": {
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||
"is_executing": true,
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# Evaluate Reader on its own\n",
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"reader_eval_results = reader.eval(document_store=document_store, device=device, label_index=label_index, doc_index=doc_index)\n",
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"# Evaluation of Reader can also be done directly on a SQuAD-formatted file without passing the data to Elasticsearch\n",
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"#reader_eval_results = reader.eval_on_file(\"../data/nq\", \"nq_dev_subset_v2.json\", device=device)\n",
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"\n",
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"## Reader Top-N-Accuracy is the proportion of predicted answers that match with their corresponding correct answer\n",
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"print(\"Reader Top-N-Accuracy:\", reader_eval_results[\"top_n_accuracy\"])\n",
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"## Reader Exact Match is the proportion of questions where the predicted answer is exactly the same as the correct answer\n",
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"print(\"Reader Exact Match:\", reader_eval_results[\"EM\"])\n",
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"## Reader F1-Score is the average overlap between the predicted answers and the correct answers\n",
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"print(\"Reader F1-Score:\", reader_eval_results[\"f1\"])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "7i84KXONOhvc",
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||
"pycharm": {
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||
"name": "#%% md\n"
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}
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},
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"source": [
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"## Evaluation of Finder"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 1000
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},
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"colab_type": "code",
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||
"id": "yLpMHAexOhvd",
|
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"outputId": "fd74be7d-5c8e-4eb9-a653-062427b74347",
|
||
"pycharm": {
|
||
"is_executing": true,
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# Evaluate combination of Reader and Retriever through Finder\n",
|
||
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