TransformersNamedEntityExtractor

This component extracts predefined entities out of a piece of text and writes them into documents’ meta field.

Most common position in a pipeline	After the PreProcessor in an indexing pipeline or after a Retriever in a query pipeline
Mandatory init variables	model: Name or path of the model to use
Mandatory run variables	documents: A list of documents
Output variables	documents: A list of documents
API reference	Transformers
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/transformers
Package name	transformers-haystack

Overview

TransformersNamedEntityExtractor looks for entities, which are spans in the text. The extractor automatically recognizes and groups them depending on their class, such as people's names, organizations, locations, and other types. The exact classes are determined by the model that you initialize the component with.

TransformersNamedEntityExtractor takes a list of documents as input and returns a list of the same documents with their meta data enriched with NamedEntityAnnotations. A NamedEntityAnnotation consists of the type of the entity, the start and end of the span, and a score calculated by the model, for example: NamedEntityAnnotation(entity='PER', start=11, end=16, score=0.9).

When the TransformersNamedEntityExtractor is initialized, you need to set a model. Optionally, you can set pipeline_kwargs, which are then passed on to the Hugging Face pipeline. You can additionally set the device that is used to run the component.

Authentication with a Hugging Face API token is only required to access private or gated models. You can pass the token at initialization with token, or set the HF_API_TOKEN or HF_TOKEN environment variable.

Usage

Install the transformers-haystack package to use the TransformersNamedEntityExtractor:

shell

pip install transformers-haystack

The component works with any Hugging Face model that supports token classification or NER.

TransformersNamedEntityExtractor accepts a list of Documents as its input. The extractor annotates the raw text in the documents and stores the annotations in the document's meta dictionary under the named_entities key.

python

from haystack.dataclasses import Document
from haystack_integrations.components.extractors.transformers import (
    TransformersNamedEntityExtractor,
)

extractor = TransformersNamedEntityExtractor(model="dslim/bert-base-NER")

documents = [
    Document(content="My name is Clara and I live in Berkeley, California."),
    Document(content="I'm Merlin, the happy pig!"),
    Document(content="New York State is home to the Empire State Building."),
]

extractor.run(documents)
print(documents)

Here is the example result:

python

[Document(id=aec840d1b6c85609f4f16c3e222a5a25fd8c4c53bd981a40c1268ab9c72cee10, content: 'My name is Clara and I live in Berkeley, California.', meta: {'named_entities': [NamedEntityAnnotation(entity='PER', start=11, end=16, score=0.99641764), NamedEntityAnnotation(entity='LOC', start=31, end=39, score=0.996198), NamedEntityAnnotation(entity='LOC', start=41, end=51, score=0.9990196)]}),
Document(id=98f1dc5d0ccd9d9950cd191d1076db0f7af40c401dd7608f11c90cb3fc38c0c2, content: 'I'm Merlin, the happy pig!', meta: {'named_entities': [NamedEntityAnnotation(entity='PER', start=4, end=10, score=0.99054915)]}),
Document(id=44948ea0eec018b33aceaaedde4616eb9e93ce075e0090ec1613fc145f84b4a9, content: 'New York State is home to the Empire State Building.', meta: {'named_entities': [NamedEntityAnnotation(entity='LOC', start=0, end=14, score=0.9989541), NamedEntityAnnotation(entity='LOC', start=30, end=51, score=0.95746297)]})]

Get stored annotations

This component includes the get_stored_annotations helper class method that allows you to retrieve the annotations stored in a Document transparently:

python

from haystack.dataclasses import Document
from haystack_integrations.components.extractors.transformers import (
    TransformersNamedEntityExtractor,
)

extractor = TransformersNamedEntityExtractor(model="dslim/bert-base-NER")

documents = [
    Document(content="My name is Clara and I live in Berkeley, California."),
    Document(content="I'm Merlin, the happy pig!"),
    Document(content="New York State is home to the Empire State Building."),
]

extractor.run(documents)

annotations = [
    TransformersNamedEntityExtractor.get_stored_annotations(doc) for doc in documents
]
print(annotations)

# If a Document doesn't contain any annotations, this returns None.
new_doc = Document(content="In one of many possible worlds...")
assert TransformersNamedEntityExtractor.get_stored_annotations(new_doc) is None