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