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Update readme

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Malte Pietsch 2020-12-26 12:14:52 +01:00 committed by GitHub
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@ -105,12 +105,13 @@ We recommend Elasticsearch or FAISS, but have also more light-weight options for
```python ```python
# DB to store your docs # DB to store your docs
document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", document_store = ElasticsearchDocumentStore(host="localhost", username="", password="",
index="document", embedding_dim=768, embedding_field="embedding") index="document", embedding_dim=768,
embedding_field="embedding")
# Index your docs # Index your docs
# (Options: Convert text from PDFs etc. via FileConverter; Split and clean docs with the PreProcessor) # (Options: Convert text from PDFs etc. via FileConverter; Split and clean docs with the PreProcessor)
docs = [Document(text="Arya accompanies her father Ned and her sister Sansa to King's Landing. Before their departure ...", meta={}), docs = [Document(text="Arya accompanies her father Ned and her sister Sansa to King's Landing. Before their departure ...", meta={}),
...] ...]
document_store.write_documents([doc]) document_store.write_documents([doc])
@ -131,7 +132,7 @@ reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=Tr
pipeline = ExtractiveQAPipeline(reader, retriever) pipeline = ExtractiveQAPipeline(reader, retriever)
# Voilá! Ask a question! # Voilá! Ask a question!
prediction = pipeline.run(query="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=3) prediction = pipeline.run(query="Who is the father of Arya Stark?", top_k_retriever=10,top_k_reader=3)
print_answers(prediction, details="minimal") print_answers(prediction, details="minimal")
[ { 'answer': 'Eddard', [ { 'answer': 'Eddard',