README.md

metadata

language:
  - en
license: apache-2.0
library_name: span-marker
tags:
  - span-marker
  - token-classification
  - ner
  - named-entity-recognition
datasets:
  - conll2003
metrics:
  - f1
  - recall
  - precision
pipeline_tag: token-classification
widget:
  - text: >-
      Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic
      to Paris.
    example_title: Amelia Earhart
base_model: xlm-roberta-large
model-index:
  - name: SpanMarker w. xlm-roberta-large on CoNLL03 by Tom Aarsen
    results:
      - task:
          type: token-classification
          name: Named Entity Recognition
        dataset:
          name: CoNLL03
          type: conll2003
          split: test
          revision: 01ad4ad271976c5258b9ed9b910469a806ff3288
        metrics:
          - type: f1
            value: 0.9307
            name: F1
          - type: precision
            value: 0.9264
            name: Precision
          - type: recall
            value: 0.935
            name: Recall

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.

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:

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-xlm-roberta-large-conll03")
# 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

# ✅
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.