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tomaarsen/
span-marker-xlm-roberta-large-conllpp-doc-context

Name: span-marker-xlm-roberta-large-conllpp-doc-context
Availability: InStock

named-entity-recognition

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SpanMarker for Named Entity Recognition

This is a SpanMarker model that can be used for Named Entity Recognition. In particular, this SpanMarker model uses xlm-roberta-large as the underlying encoder. See train.py for the training script. Note that this model was trained with document-level context, i.e. it will primarily perform well when provided with enough context. It is recommended to call model.predict with a 🤗 Dataset with tokens, document_id and sentence_id columns. See the documentation of the model.predict method for more information.

Usage

To use this model for inference, first install the span_marker library:

pip install span_marker

You can then run inference with this model like so:

python

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-xlm-roberta-large-conllpp-doc-context")
# Run inference
entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.")

Limitations

Warning: This model works best when punctuation is separated from the prior words, so

python

# ✅
model.predict("He plays J. Robert Oppenheimer , an American theoretical physicist .")
# ❌
model.predict("He plays J. Robert Oppenheimer, an American theoretical physicist.")

# You can also supply a list of words directly: ✅
model.predict(["He", "plays", "J.", "Robert", "Oppenheimer", ",", "an", "American", "theoretical", "physicist", "."])

The same may be beneficial for some languages, such as splitting "l'ocean Atlantique" into "l' ocean Atlantique".

See the SpanMarker repository for documentation and additional information on this library.