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1718ea55b8
* initial version of training a reader WIP * update for latest changes in FARM inferencer. Update tutorial. Add basic docs
56 lines
2.4 KiB
Python
Executable File
56 lines
2.4 KiB
Python
Executable File
from haystack.reader.farm import FARMReader
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from haystack.reader.transformers import TransformersReader
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from haystack.retriever.tfidf import TfidfRetriever
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from haystack import Finder
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from haystack.indexing.io import write_documents_to_db, fetch_archive_from_http
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from haystack.indexing.cleaning import clean_wiki_text
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from haystack.utils import print_answers
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## Indexing & cleaning documents
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# Init a database (default: sqllite)
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from haystack.database import db
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db.create_all()
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# Let's first get some documents that we want to query
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# Here: 517 Wikipedia articles for Game of Thrones
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doc_dir = "data/article_txt_got"
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s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
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fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
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# Now, let's write the docs to our DB.
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# You can supply a cleaning function that is applied to each doc (e.g. to remove footers)
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# It must take a str as input, and return a str.
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write_documents_to_db(document_dir=doc_dir, clean_func=clean_wiki_text, only_empty_db=True)
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## Initalize Reader, Retriever & Finder
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# A retriever identifies the k most promising chunks of text that might contain the answer for our question
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# Retrievers use some simple but fast algorithm, here: TF-IDF
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retriever = TfidfRetriever()
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# A reader scans the text chunks in detail and extracts the k best answers
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# Reader use more powerful but slower deep learning models, here: a BERT QA model trained via FARM on Squad 2.0
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fetch_archive_from_http(url="https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-models/0.3.0/bert-english-qa-large.tar.gz", output_dir="model")
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reader = FARMReader(model_name_or_path="model/bert-english-qa-large", use_gpu=False)
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# OR: use alternatively a reader from huggingface's Transformers package
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# reader = TransformersReader(use_gpu=-1)
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# The Finder sticks together retriever and retriever in a pipeline to answer our actual questions
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finder = Finder(reader, retriever)
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## Voilá! Ask a question!
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# You can configure how many candidates the reader and retriever shall return
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# The higher top_k_retriever, the better (but also the slower) your answers.
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prediction = finder.get_answers(question="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=5)
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#prediction = finder.get_answers(question="Who created the Dothraki vocabulary?", top_k_reader=5)
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#prediction = finder.get_answers(question="Who is the sister of Sansa?", top_k_reader=5)
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print_answers(prediction, details="minimal")
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