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 by Tom Aarsen
results:
- task:
type: token-classification
name: Named Entity Recognition
dataset:
type: conll2003
name: CoNLL03
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
datasets:
- 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.
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.