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.
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Model tree for tomaarsen/span-marker-xlm-roberta-large-conll03
Base model
FacebookAI/xlm-roberta-largeDataset used to train tomaarsen/span-marker-xlm-roberta-large-conll03
Evaluation results
- F1 on CoNLL03test set self-reported0.931
- Precision on CoNLL03test set self-reported0.926
- Recall on CoNLL03test set self-reported0.935