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d1f8b7118c
* Add progress bar to batch run component ops * Update docs * Update schema * PR review: thanks Bogdan
82 lines
2.1 KiB
Markdown
82 lines
2.1 KiB
Markdown
<a id="entity"></a>
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# Module entity
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<a id="entity.EntityExtractor"></a>
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## EntityExtractor
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```python
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class EntityExtractor(BaseComponent)
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```
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This node is used to extract entities out of documents.
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The most common use case for this would be as a named entity extractor.
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The default model used is dslim/bert-base-NER.
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This node can be placed in a querying pipeline to perform entity extraction on retrieved documents only,
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or it can be placed in an indexing pipeline so that all documents in the document store have extracted entities.
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The entities extracted by this Node will populate Document.entities
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**Arguments**:
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- `model_name_or_path`: The name of the model to use for entity extraction.
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- `use_gpu`: Whether to use the GPU or not.
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- `batch_size`: The batch size to use for entity extraction.
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- `progress_bar`: Whether to show a progress bar or not.
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<a id="entity.EntityExtractor.run"></a>
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#### EntityExtractor.run
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```python
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def run(documents: Optional[Union[List[Document], List[dict]]] = None) -> Tuple[Dict, str]
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```
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This is the method called when this node is used in a pipeline
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<a id="entity.EntityExtractor.extract"></a>
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#### EntityExtractor.extract
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```python
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def extract(text)
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```
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This function can be called to perform entity extraction when using the node in isolation.
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<a id="entity.EntityExtractor.extract_batch"></a>
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#### EntityExtractor.extract\_batch
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```python
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def extract_batch(texts: Union[List[str], List[List[str]]], batch_size: Optional[int] = None)
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```
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This function allows to extract entities out of a list of strings or a list of lists of strings.
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**Arguments**:
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- `texts`: List of str or list of lists of str to extract entities from.
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- `batch_size`: Number of texts to make predictions on at a time.
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<a id="entity.simplify_ner_for_qa"></a>
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#### simplify\_ner\_for\_qa
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```python
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def simplify_ner_for_qa(output)
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```
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Returns a simplified version of the output dictionary
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with the following structure:
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[
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{
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answer: { ... }
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entities: [ { ... }, {} ]
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}
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]
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The entities included are only the ones that overlap with
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the answer itself.
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