tomaarsen's picture
tomaarsen HF staff
Add limitation due to RoBERTa
license: apache-2.0
library_name: span-marker
  - span-marker
  - token-classification
  - ner
  - named-entity-recognition
pipeline_tag: token-classification
  - text: >-
      Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic
      to Paris.
    example_title: Amelia Earhart
  - text: >-
      Leonardo di ser Piero da Vinci painted the Mona Lisa based on Italian
      noblewoman Lisa del Giocondo.
    example_title: Leonardo da Vinci
  - text: >-
      On June 13th, 2014, at 4:44 pm during the 2014 World Cup held in Salvador,
      Brazil, the legendary soccer player, Robin van Persie, representing the
      Dutch national team, scored a remarkable goal in the 44th minute.
    example_title: Robin van Persie
  - name: SpanMarker w. roberta-large on OntoNotes v5.0 by Tom Aarsen
      - task:
          type: token-classification
          name: Named Entity Recognition
          type: tner/ontonotes5
          name: OntoNotes v5.0
          split: test
          revision: cf9ef57ad260810be1298ba795d83c09a915e959
          - type: f1
            value: 0.9153
            name: F1
          - type: precision
            value: 0.9116
            name: Precision
          - type: recall
            value: 0.9191
            name: Recall
  - tner/ontonotes5
  - en
  - f1
  - recall
  - precision

SpanMarker for Named Entity Recognition

This is a SpanMarker model that can be used for Named Entity Recognition. In particular, this SpanMarker model uses roberta-large as the underlying encoder. See for the training script.


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:

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-roberta-large-ontonotes5")
# Run inference
entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.")


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

# ✅
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.