mirror of
https://github.com/deepset-ai/haystack.git
synced 2025-07-22 16:31:16 +00:00
f2b6cc761b
* change_HFBertEncoder to transformers DPREncoder * Removed BertTensorizer * model download relative path * Refactor model load * Tutorial5 DPR updated * fix print_eval_results typo * copy transformers DPR modules in dpr_utils and test * transformer v3.0.2 import errors fixed * remove dependency of DPRConfig on attribute use_return_tuple * Adjust transformers 302 locally to work with dpr * projection layer removed from DPR encoders * fixed mypy errors * transformers DPR compatible code added * transformers DPR compatibility added * bug fix in tutorial 6 notebook * Docstring update and variable naming issues fix * tutorial modified to reflect DPR variable naming change * title addition to passage use-cases handled * modified handling untitled batch * resolved mypy errors * typos in docstrings and comments fixed * cleaned DPR code and added new test cases * warnings added for non-bert model [SEP] token removal * changed warning to logger warning * title mask creation refactored * bug fix on cuda issues * tutorial 6 instantiates modified DPR * tutorial 5 modified * tutorial 5 ipython notebook modified: DPR instantiation * batch_size added to DPR instantiation * tutorial 5 jupyter notebook typos fixed * improved docstrings, fixed typos * Update docstring Co-authored-by: Timo Moeller <timo.moeller@deepset.ai> Co-authored-by: Malte Pietsch <malte.pietsch@deepset.ai>
1961 lines
58 KiB
Plaintext
1961 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\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 and install the version of torch that works with the colab GPUs\n",
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"!pip install git+https://github.com/deepset-ai/haystack.git\n",
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"!pip install torch==1.5.1+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html"
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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-7.6.2-linux-x86_64.tar.gz -q\n",
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"! tar -xzf elasticsearch-7.6.2-linux-x86_64.tar.gz\n",
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"! chown -R daemon:daemon elasticsearch-7.6.2\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-7.6.2/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.indexing.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.database.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 database\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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"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\")"
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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",
|
||
"outputId": "24d3755e-bf2e-4396-f1a2-59c925cc54d3",
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||
"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 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",
|
||
"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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