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* Files moved, imports all broken * Fix most imports and docstrings into * Fix the paths to the modules in the API docs * Add latest docstring and tutorial changes * Add a few pipelines that were lost in the inports * Fix a bunch of mypy warnings * Add latest docstring and tutorial changes * Create a file_classifier module * Add docs for file_classifier * Fixed most circular imports, now the REST API can start * Add latest docstring and tutorial changes * Tackling more mypy issues * Reintroduce from FARM and fix last mypy issues hopefully * Re-enable old-style imports * Fix some more import from the top-level package in an attempt to sort out circular imports * Fix some imports in tests to new-style to prevent failed class equalities from breaking tests * Change document_store into document_stores * Update imports in tutorials * Add latest docstring and tutorial changes * Probably fixes summarizer tests * Improve the old-style import allowing module imports (should work) * Try to fix the docs * Remove dedicated KnowledgeGraph page from autodocs * Remove dedicated GraphRetriever page from autodocs * Fix generate_docstrings.sh with an updated list of yaml files to look for * Fix some more modules in the docs * Fix the document stores docs too * Fix a small issue on Tutorial14 * Add latest docstring and tutorial changes * Add deprecation warning to old-style imports * Remove stray folder and import Dict into dense.py * Change import path for MLFlowLogger * Add old loggers path to the import path aliases * Fix debug output of convert_ipynb.py * Fix circular import on BaseRetriever * Missed one merge block * re-run tutorial 5 * Fix imports in tutorial 5 * Re-enable squad_to_dpr CLI from the root package and move get_batches_from_generator into document_stores.base * Add latest docstring and tutorial changes * Fix typo in utils __init__ * Fix a few more imports * Fix benchmarks too * New-style imports in test_knowledge_graph * Rollback setup.py * Rollback squad_to_dpr too Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
17161 lines
671 KiB
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17161 lines
671 KiB
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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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||
"[](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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"collapsed": true,
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"id": "MGSXn0USOhtu",
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}
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{
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"cell_type": "markdown",
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"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",
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||
"\n",
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"<img src=\"https://raw.githubusercontent.com/deepset-ai/haystack/master/docs/_src/img/colab_gpu_runtime.jpg\">"
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],
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"metadata": {
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"source": [
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"# Make sure you have a GPU running\n",
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||
"!nvidia-smi"
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],
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Mon Oct 25 12:02:00 2021 \n",
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"+-----------------------------------------------------------------------------+\n",
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"| NVIDIA-SMI 470.74 Driver Version: 460.32.03 CUDA Version: 11.2 |\n",
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"|-------------------------------+----------------------+----------------------+\n",
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"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
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"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
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"| | | MIG M. |\n",
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"|===============================+======================+======================|\n",
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"| 0 Tesla K80 Off | 00000000:00:04.0 Off | 0 |\n",
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"| N/A 35C P8 27W / 149W | 0MiB / 11441MiB | 0% Default |\n",
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"| | | N/A |\n",
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"+-------------------------------+----------------------+----------------------+\n",
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" \n",
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"+-----------------------------------------------------------------------------+\n",
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"| Processes: |\n",
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"| GPU GI CI PID Type Process name GPU Memory |\n",
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"| ID ID Usage |\n",
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"|=============================================================================|\n",
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"| No running processes found |\n",
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"+-----------------------------------------------------------------------------+\n"
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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"outputId": "285b2491-01e5-4bfd-cba9-c2279d4417c4",
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"colab": {
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"base_uri": "https://localhost:8080/"
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||
}
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},
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{
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||
"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."
|
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],
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||
"metadata": {
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"source": [
|
||
"# Install the latest release of Haystack in your own environment \n",
|
||
"#! pip install farm-haystack\n",
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||
"\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."
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],
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"outputs": [
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"\u001b[?25hRequirement already satisfied: inflect in /usr/local/lib/python3.7/dist-packages (from quantulum3->farm-haystack==1.0.0rc1) (2.1.0)\n",
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"Requirement already satisfied: docopt>=0.6.2 in /usr/local/lib/python3.7/dist-packages (from num2words->quantulum3->farm-haystack==1.0.0rc1) (0.6.2)\n",
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"Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==4.7.0->farm-haystack==1.0.0rc1) (1.0.1)\n",
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"Collecting rdflib>=4.0\n",
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" Downloading rdflib-6.0.2-py3-none-any.whl (407 kB)\n",
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"\u001b[K |████████████████████████████████| 407 kB 45.9 MB/s \n",
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"\u001b[?25hCollecting virtualenv!=20.0.0,!=20.0.1,!=20.0.2,!=20.0.3,!=20.0.4,!=20.0.5,!=20.0.6,!=20.0.7,>=16.0.0\n",
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" Downloading virtualenv-20.9.0-py2.py3-none-any.whl (5.6 MB)\n",
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"Requirement already satisfied: toml>=0.9.4 in /usr/local/lib/python3.7/dist-packages (from tox->farm-haystack==1.0.0rc1) (0.10.2)\n",
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"Requirement already satisfied: py>=1.4.17 in /usr/local/lib/python3.7/dist-packages (from tox->farm-haystack==1.0.0rc1) (1.10.0)\n",
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" Downloading distlib-0.3.3-py2.py3-none-any.whl (496 kB)\n",
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"\u001b[K |████████████████████████████████| 496 kB 51.2 MB/s \n",
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"\u001b[?25hCollecting h11>=0.8\n",
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"\u001b[K |████████████████████████████████| 54 kB 3.2 MB/s \n",
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"\u001b[?25hCollecting asgiref>=3.4.0\n",
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" Downloading asgiref-3.4.1-py3-none-any.whl (25 kB)\n",
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"Building wheels for collected packages: farm-haystack, pytesseract, alembic, databricks-cli, sentence-transformers, gpustat, langdetect, python-docx, python-multipart, seqeval, tika\n",
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" Building wheel for farm-haystack (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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" Created wheel for farm-haystack: filename=farm_haystack-1.0.0rc1-py3-none-any.whl size=343843 sha256=db4b7454c22f16b3e7a42d15f6080cc1a6e520b05d1de2e2b47dc818df5e4524\n",
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" Stored in directory: /tmp/pip-ephem-wheel-cache-s3i7tkob/wheels/a7/05/3b/9b33368d9af06a39f8e6af2e97fa2af876e893ade323cfc2c9\n",
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" Created wheel for pytesseract: filename=pytesseract-0.3.7-py2.py3-none-any.whl size=13954 sha256=a7384f54aebc426b35ef6cea754bbeb14d85cb0ed3c42031ddc843b67135bc95\n",
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" Stored in directory: /root/.cache/pip/wheels/67/71/6c/7a8c5ca2e699752506999ae7baeb692e2b4fc6488c2cddcb22\n",
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" Created wheel for alembic: filename=alembic-1.4.1-py2.py3-none-any.whl size=158172 sha256=8e165a10131570340768847c36fe37dbc5aad8b21b85e31ee76a8bca6806c14b\n",
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" Stored in directory: /root/.cache/pip/wheels/be/5d/0a/9e13f53f4f5dfb67cd8d245bb7cdffe12f135846f491a283e3\n",
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" Building wheel for databricks-cli (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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" Created wheel for databricks-cli: filename=databricks_cli-0.16.2-py3-none-any.whl size=106811 sha256=fbaedb547d61c892ee81b7d0077ec7fb626d4f05f32fe2abb2beeb5df4daeeee\n",
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" Stored in directory: /root/.cache/pip/wheels/f4/5c/ed/e1ce20a53095f63b27b4964abbad03e59cf3472822addf7d29\n",
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" Created wheel for sentence-transformers: filename=sentence_transformers-2.1.0-py3-none-any.whl size=121000 sha256=493e12bc419d20a35c1fea5ec8fd37101ccfd77ac9525057d2bed5c429961d39\n",
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" Stored in directory: /root/.cache/pip/wheels/90/f0/bb/ed1add84da70092ea526466eadc2bfb197c4bcb8d4fa5f7bad\n",
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" Created wheel for gpustat: filename=gpustat-0.6.0-py3-none-any.whl size=12617 sha256=59857b12cb4a13c77bd4127921c88b45f29c64b3391a16d2dab09bf24b7305b1\n",
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" Stored in directory: /root/.cache/pip/wheels/e6/67/af/f1ad15974b8fd95f59a63dbf854483ebe5c7a46a93930798b8\n",
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" Created wheel for langdetect: filename=langdetect-1.0.9-py3-none-any.whl size=993242 sha256=6f6cb9ee9b2a5ee3930285b521b2a70e63b0be49921560f546f649ef7d1094bc\n",
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" Stored in directory: /root/.cache/pip/wheels/c5/96/8a/f90c59ed25d75e50a8c10a1b1c2d4c402e4dacfa87f3aff36a\n",
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" Building wheel for python-docx (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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" Created wheel for python-docx: filename=python_docx-0.8.11-py3-none-any.whl size=184508 sha256=5eff3377f94b0946d0688f0447febc586930ea381e70a5f16eac38d39c235421\n",
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" Stored in directory: /root/.cache/pip/wheels/f6/6f/b9/d798122a8b55b74ad30b5f52b01482169b445fbb84a11797a6\n",
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" Building wheel for python-multipart (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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" Created wheel for python-multipart: filename=python_multipart-0.0.5-py3-none-any.whl size=31678 sha256=0492c5e0d3d6feb0bc9348ac2e8e1a19ce1887ce063e6b73aa022d3da24ca832\n",
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" Stored in directory: /root/.cache/pip/wheels/2c/41/7c/bfd1c180534ffdcc0972f78c5758f89881602175d48a8bcd2c\n",
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" Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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" Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16181 sha256=def2419cd582fa55399056ac684764a30b871423d2695d24a520a42405c8eb46\n",
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" Stored in directory: /root/.cache/pip/wheels/05/96/ee/7cac4e74f3b19e3158dce26a20a1c86b3533c43ec72a549fd7\n",
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" Building wheel for tika (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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" Created wheel for tika: filename=tika-1.24-py3-none-any.whl size=32891 sha256=75209167e6dd491a1862992f14e4af3c15df646a6bafb2414fcc39e80df07cfa\n",
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" Stored in directory: /root/.cache/pip/wheels/ec/2b/38/58ff05467a742e32f67f5d0de048fa046e764e2fbb25ac93f3\n",
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"Successfully built farm-haystack pytesseract alembic databricks-cli sentence-transformers gpustat langdetect python-docx python-multipart seqeval tika\n",
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"Installing collected packages: multidict, yarl, sniffio, smmap, isodate, async-timeout, websocket-client, tokenizers, sacremoses, python-editor, platformdirs, pillow, opencensus-context, mypy-extensions, msrest, marshmallow, Mako, huggingface-hub, grpcio, gitdb, distlib, cryptography, blessings, backports.entry-points-selectable, azure-core, anyio, aiohttp, virtualenv, validators, ujson, typing-inspect, transformers, starlette, sentencepiece, redis, rdflib, querystring-parser, pydantic, py-spy, prometheus-flask-exporter, pluggy, opencensus, num2words, marshmallow-enum, h11, gunicorn, gpustat, 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",
|
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" Attempting uninstall: pillow\n",
|
||
" Found existing installation: Pillow 7.1.2\n",
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" Uninstalling Pillow-7.1.2:\n",
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" Successfully uninstalled Pillow-7.1.2\n",
|
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" Attempting uninstall: grpcio\n",
|
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" Found existing installation: grpcio 1.41.0\n",
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" Uninstalling grpcio-1.41.0:\n",
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" Successfully uninstalled grpcio-1.41.0\n",
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" Attempting uninstall: pluggy\n",
|
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" Found existing installation: pluggy 0.7.1\n",
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||
" Uninstalling pluggy-0.7.1:\n",
|
||
" Successfully uninstalled pluggy-0.7.1\n",
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"\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",
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||
"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",
|
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"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"
|
||
]
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},
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{
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"output_type": "display_data",
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"data": {
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"application/vnd.colab-display-data+json": {
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"pip_warning": {
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"packages": [
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||
"PIL"
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||
]
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||
}
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||
}
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},
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||
"metadata": {}
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||
}
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||
],
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||
"metadata": {
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||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 1000
|
||
},
|
||
"id": "vgmFOp82Oht_",
|
||
"pycharm": {
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||
"name": "#%%\n"
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},
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"outputId": "5bbcbb42-3a90-43a9-ebfd-598a98fa7143"
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||
}
|
||
},
|
||
{
|
||
"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",
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"pycharm": {
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"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",
|
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"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "118aedffeecd4f109ae04a4561baeb08",
|
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"version_minor": 0,
|
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"version_major": 2
|
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},
|
||
"text/plain": [
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"metadata": {}
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{
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"output_type": "display_data",
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "6f7ddc1720344ba9b939a8e4ac593d67",
|
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"version_minor": 0,
|
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"version_major": 2
|
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},
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"text/plain": [
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},
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"metadata": {}
|
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},
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{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"Some weights of the model checkpoint at deepset/roberta-base-squad2 were not used when initializing RobertaModel: ['qa_outputs.bias', 'qa_outputs.weight']\n",
|
||
"- This IS expected if you are initializing RobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
|
||
"- This IS NOT expected if you are initializing RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
|
||
"Some weights of RobertaModel were not initialized from the model checkpoint at deepset/roberta-base-squad2 and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
|
||
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
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"application/vnd.jupyter.widget-view+json": {
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|
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"version_minor": 0,
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"output_type": "display_data",
|
||
"data": {
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"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "5db857b352964db3a617568ff1dce86d",
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"output_type": "display_data",
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "36f855f41cc1488f9d92ec34bb8d30b0",
|
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"output_type": "display_data",
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"data": {
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"metadata": {}
|
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},
|
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{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"ML Logging is turned off. No parameters, metrics or artifacts will be logged to MLFlow.\n"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 313,
|
||
"referenced_widgets": [
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"118aedffeecd4f109ae04a4561baeb08",
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"ea439e2251ed467fb3a775f0c8e0c3bb",
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"5f94d400ea884c1cadfc966e44849b3a",
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"0d34710578ac4c1db6fe372b5d1215b4",
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"994ae85181664e2e87a2ee18a7a237ba",
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"ccfd1a0b6f494d8a9d78e7387261fba8",
|
||
"db57281b3d7a448fbd2d63d8f127ea3e",
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"978b165c69dd4e14b8479ea7bd8cb1e5",
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"6f028c7e888e4ae5ab5c1e42ff142b5f",
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"c8ba8c2a210b45f6a9b5257589babac3",
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||
"1d5d220bedc54dbdbacb9c43767bf64d",
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||
"0523b10429d04f3d81d7078a13a12168",
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"64cd5b6f0c4d4631a1049ee7ee50f063",
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||
"eb11ea5785284bf6a15cc31ad643ed88",
|
||
"807e4eee3b2c440c8799afcc6344ff5d",
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||
"6ca6dc2c6b4349fcb39ed8c44f65bdb0",
|
||
"4837b34ccb4d4688865dc24dc58a7c1e",
|
||
"d4dbfa5e89e7432dbed34606a786fd6f",
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||
"7e058076836e438daf5399428eabac5e",
|
||
"a529dbbfdd6b469dbfe80cee993c9a33",
|
||
"69750fea2e7149eab8928282ba9bae29",
|
||
"08a1d1a6fb884c769d409170d6cda556",
|
||
"548ef6c85056414cb0ce79164a086d35",
|
||
"cf58b340496b4d62b610451cedbd709a",
|
||
"4dad7e58cc47436aafe38230514325a1",
|
||
"f36dc87a4af7481cb3c2cba23d57eb5a",
|
||
"2a9ef1f2f43d47b28cd0ff7ef4a21ade",
|
||
"caa374f7dc5045218c6f71f322d8e6be",
|
||
"e567fab4446544f795be2eb0a6705f9c",
|
||
"2b17ffac93b8406fac55b695d93d963b",
|
||
"3096cae7388e4b988df306be9cc58afd"
|
||
]
|
||
},
|
||
"id": "cW3Ypn_gOhvK",
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
},
|
||
"outputId": "4b5feff7-ae9f-4cd8-de1e-944f0eb66f66"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"source": [
|
||
"from haystack.nodes import EvalAnswers, EvalDocuments\n",
|
||
"\n",
|
||
"# Here we initialize the nodes that perform evaluation\n",
|
||
"eval_retriever = EvalDocuments()\n",
|
||
"eval_reader = EvalAnswers(sas_model=\"sentence-transformers/paraphrase-multilingual-mpnet-base-v2\")"
|
||
],
|
||
"outputs": [],
|
||
"metadata": {
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
},
|
||
"id": "NRq01w8iU7tN"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"## Evaluation of Retriever\n",
|
||
"Here we evaluate only the retriever, based on whether the gold_label document is retrieved."
|
||
],
|
||
"metadata": {
|
||
"id": "qwkBgzh5OhvR",
|
||
"pycharm": {
|
||
"name": "#%% md\n"
|
||
}
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"source": [
|
||
"## Evaluate Retriever on its own\n",
|
||
"retriever_eval_results = retriever.eval(top_k=20, label_index=label_index, doc_index=doc_index)\n",
|
||
"## Retriever Recall is the proportion of questions for which the correct document containing the answer is\n",
|
||
"## among the correct documents\n",
|
||
"print(\"Retriever Recall:\", retriever_eval_results[\"recall\"])\n",
|
||
"## Retriever Mean Avg Precision rewards retrievers that give relevant documents a higher rank\n",
|
||
"print(\"Retriever Mean Avg Precision:\", retriever_eval_results[\"map\"])"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"100%|██████████| 771/771 [00:07<00:00, 106.27it/s]\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Retriever Recall: 0.32814526588845655\n",
|
||
"Retriever Mean Avg Precision: 0.09174006526330329\n"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "YzvLhnx3OhvS",
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
},
|
||
"outputId": "ca5bbc09-6407-4d57-f234-f14ee26aca96"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"## Evaluation of Reader\n",
|
||
"Here we evaluate only the reader in a closed domain fashion i.e. the reader is given one query\n",
|
||
"and one document and metrics are calculated on whether the right position in this text is selected by\n",
|
||
"the model as the answer span (i.e. SQuAD style)"
|
||
],
|
||
"metadata": {
|
||
"id": "fjZRnB6bOhvW",
|
||
"pycharm": {
|
||
"name": "#%% md\n"
|
||
}
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"source": [
|
||
"# Evaluate Reader on its own\n",
|
||
"reader_eval_results = reader.eval(document_store=document_store, device=device, label_index=label_index, doc_index=doc_index)\n",
|
||
"# Evaluation of Reader can also be done directly on a SQuAD-formatted file without passing the data to Elasticsearch\n",
|
||
"#reader_eval_results = reader.eval_on_file(\"../data/nq\", \"nq_dev_subset_v2.json\", device=device)\n",
|
||
"\n",
|
||
"## Reader Top-N-Accuracy is the proportion of predicted answers that match with their corresponding correct answer\n",
|
||
"print(\"Reader Top-N-Accuracy:\", reader_eval_results[\"top_n_accuracy\"])\n",
|
||
"## Reader Exact Match is the proportion of questions where the predicted answer is exactly the same as the correct answer\n",
|
||
"print(\"Reader Exact Match:\", reader_eval_results[\"EM\"])\n",
|
||
"## Reader F1-Score is the average overlap between the predicted answers and the correct answers\n",
|
||
"print(\"Reader F1-Score:\", reader_eval_results[\"f1\"])"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"\rEvaluating: 0%| | 0/32 [00:00<?, ?it/s]/usr/local/lib/python3.7/dist-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values.\n",
|
||
"To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at /pytorch/aten/src/ATen/native/BinaryOps.cpp:467.)\n",
|
||
" return torch.floor_divide(self, other)\n",
|
||
"Evaluating: 100%|██████████| 32/32 [00:45<00:00, 1.41s/it]"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Reader Top-N-Accuracy: 98.94875164257556\n",
|
||
"Reader Exact Match: 84.62549277266754\n",
|
||
"Reader F1-Score: 85.16284899931244\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "Lgsgf4KaOhvY",
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
},
|
||
"outputId": "aac2042b-8638-48ec-d4da-5e8923ebdebc"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"## Evaluation of Retriever and Reader (Open Domain)\n",
|
||
"Here we evaluate retriever and reader in open domain fashion i.e. a document is considered\n",
|
||
"correctly retrieved if it contains the answer string within it. The reader is evaluated based purely on the\n",
|
||
"predicted string, regardless of which document this came from and the position of the extracted span."
|
||
],
|
||
"metadata": {
|
||
"id": "7i84KXONOhvc",
|
||
"pycharm": {
|
||
"name": "#%% md\n"
|
||
}
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"source": [
|
||
"from haystack import Pipeline\n",
|
||
"\n",
|
||
"# Here is the pipeline definition\n",
|
||
"p = Pipeline()\n",
|
||
"p.add_node(component=retriever, name=\"Retriever\", inputs=[\"Query\"])\n",
|
||
"p.add_node(component=eval_retriever, name=\"EvalRetriever\", inputs=[\"Retriever\"])\n",
|
||
"p.add_node(component=reader, name=\"Reader\", inputs=[\"EvalRetriever\"])\n",
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"p.add_node(component=eval_reader, name=\"EvalReader\", inputs=[\"Reader\"])\n",
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"results = []"
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],
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},
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{
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"source": [
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"# This is how to run the pipeline\n",
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"for l in labels:\n",
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" res = p.run(\n",
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" query=l.query,\n",
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" labels=l,\n",
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" params={\"index\": doc_index, \"Retriever\": {\"top_k\": 10}, \"Reader\": {\"top_k\": 5}},\n",
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" )\n",
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" results.append(res)"
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],
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]
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}
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||
}
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||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"source": [
|
||
"# When we have run evaluation using the pipeline, we can print the results\n",
|
||
"n_queries = len(labels)\n",
|
||
"eval_retriever.print()\n",
|
||
"print()\n",
|
||
"retriever.print_time()\n",
|
||
"print()\n",
|
||
"eval_reader.print(mode=\"reader\")\n",
|
||
"print()\n",
|
||
"reader.print_time()\n",
|
||
"print()\n",
|
||
"eval_reader.print(mode=\"pipeline\")"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"EvalRetriever\n",
|
||
"-----------------\n",
|
||
"has_answer recall@10: 0.9200 (23/25)\n",
|
||
"no_answer recall@10: 1.00 (25/25) (no_answer samples are always treated as correctly retrieved)\n",
|
||
"has_answer mean_reciprocal_rank@10: 0.6110\n",
|
||
"no_answer mean_reciprocal_rank@10: 1.0000 (no_answer samples are always treated as correctly retrieved at rank 1)\n",
|
||
"recall@10: 0.9600 (48 / 50)\n",
|
||
"mean_reciprocal_rank@10: 0.8055\n",
|
||
"\n",
|
||
"Retriever (Speed)\n",
|
||
"---------------\n",
|
||
"No indexing performed via Retriever.run()\n",
|
||
"Queries Performed: 50\n",
|
||
"Query time: 0.5041560229994957s\n",
|
||
"0.010083120459989913 seconds per query\n",
|
||
"\n",
|
||
"Reader\n",
|
||
"-----------------\n",
|
||
"has answer queries: 23\n",
|
||
"top 1 EM: 0.2609\n",
|
||
"top k EM: 0.5217\n",
|
||
"top 1 F1: 0.3364\n",
|
||
"top k F1: 0.6180\n",
|
||
"top 1 SAS: 0.5375\n",
|
||
"top k SAS: 0.7885\n",
|
||
"\n",
|
||
"no_answer queries: 25\n",
|
||
"top 1 no_answer accuracy: 0.0000\n",
|
||
"\n",
|
||
"Reader (Speed)\n",
|
||
"---------------\n",
|
||
"Queries Performed: 50\n",
|
||
"Query time: 80.12184924100029s\n",
|
||
"1.6024369848200057 seconds per query\n",
|
||
"\n",
|
||
"Pipeline\n",
|
||
"-----------------\n",
|
||
"queries: 50\n",
|
||
"top 1 EM: 0.1200\n",
|
||
"top k EM: 0.7400\n",
|
||
"top 1 F1: 0.1548\n",
|
||
"top k F1: 0.7843\n",
|
||
"top 1 SAS: 0.2473\n",
|
||
"top k SAS: 0.8627\n",
|
||
"(top k results are likely inflated since the Reader always returns a no_answer prediction in its top k)\n"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"pycharm": {
|
||
"name": "#%%\n"
|
||
},
|
||
"id": "WMj1gw2NU7ta",
|
||
"outputId": "1b539dba-18c8-41a9-9bb2-b519be1061fb",
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
}
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"## About us\n",
|
||
"\n",
|
||
"This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
|
||
"\n",
|
||
"We bring NLP to the industry via open source! \n",
|
||
"Our focus: Industry specific language models & large scale QA systems. \n",
|
||
" \n",
|
||
"Some of our other work: \n",
|
||
"- [German BERT](https://deepset.ai/german-bert)\n",
|
||
"- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
|
||
"- [FARM](https://github.com/deepset-ai/FARM)\n",
|
||
"\n",
|
||
"Get in touch:\n",
|
||
"[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
|
||
"\n",
|
||
"By the way: [we're hiring!](https://www.deepset.ai/jobs)"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"id": "8QJ68G12U7tb"
|
||
}
|
||
}
|
||
]
|
||
} |