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Update README.rst

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Tanay Soni 2020-06-08 16:37:03 +02:00 committed by GitHub
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@ -135,6 +135,7 @@ The default :code:`ElasticsearchRetriever` uses Elasticsearch's native scoring (
Example:: Example::
.. code-block:: python
retriever = ElasticsearchRetriever(document_store=document_store, custom_query=None) retriever = ElasticsearchRetriever(document_store=document_store, custom_query=None)
retriever.retrieve(query="Why did the revenue increase?", filters={"years": ["2019"], "company": ["Q1", "Q2"]}) retriever.retrieve(query="Why did the revenue increase?", filters={"years": ["2019"], "company": ["Q1", "Q2"]})
# returns: [Document, Document] # returns: [Document, Document]
@ -146,6 +147,7 @@ This retriever allows you to transform your query into an embedding using a mode
Example:: Example::
.. code-block:: python
retriever = EmbeddingRetriever(document_store=document_store, retriever = EmbeddingRetriever(document_store=document_store,
embedding_model="deepset/sentence-bert", embedding_model="deepset/sentence-bert",
model_format="farm") model_format="farm")
@ -168,6 +170,7 @@ FARMReader
Implementing various QA models via the `FARM <https://github.com/deepset-ai/FARM>`_ Framework. Implementing various QA models via the `FARM <https://github.com/deepset-ai/FARM>`_ Framework.
Example:: Example::
.. code-block:: python
reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2",
use_gpu=False, no_ans_boost=-10, context_window_size=500, use_gpu=False, no_ans_boost=-10, context_window_size=500,
top_k_per_candidate=3, top_k_per_sample=1, top_k_per_candidate=3, top_k_per_sample=1,
@ -193,6 +196,7 @@ Implementing various QA models via the :code:`pipeline` class of `Transformers <
Example:: Example::
.. code-block:: python
reader = TransformersReader(model="distilbert-base-uncased-distilled-squad", reader = TransformersReader(model="distilbert-base-uncased-distilled-squad",
tokenizer="distilbert-base-uncased", tokenizer="distilbert-base-uncased",
context_window_size=500, context_window_size=500,
@ -235,4 +239,4 @@ Haystack has a customizable PDF text extraction pipeline with cleaning functions
8. Development 8. Development
------------------- -------------------
* Unit tests can be executed by running :code:`tox`. * Unit tests can be executed by running :code:`tox`.