README.md

metadata

license: apache-2.0
library_name: span-marker
tags:
  - span-marker
  - token-classification
  - ner
  - named-entity-recognition
pipeline_tag: token-classification
widget:
  - text: >-
      Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic
      to Paris.
    example_title: Amelia Earhart
model-index:
  - name: >-
      SpanMarker w. xlm-roberta-large on CoNLL03 with document-level context by
      Tom Aarsen
    results:
      - task:
          type: token-classification
          name: Named Entity Recognition
        dataset:
          type: conll2003
          name: CoNLL03 w. document context
          split: test
          revision: 01ad4ad271976c5258b9ed9b910469a806ff3288
        metrics:
          - type: f1
            value: 0.9442
            name: F1
          - type: precision
            value: 0.9411
            name: Precision
          - type: recall
            value: 0.9473
            name: Recall
datasets:
  - conll2003
  - tomaarsen/conll2003
language:
  - en
metrics:
  - 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 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:

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-xlm-roberta-large-conll03-doc-context")
# Run inference
entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.")

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