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
- 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
model-index:
- name: SpanMarker w. roberta-large on OntoNotes v5.0 by Tom Aarsen
results:
- task:
type: token-classification
name: Named Entity Recognition
dataset:
type: tner/ontonotes5
name: OntoNotes v5.0
split: test
revision: cf9ef57ad260810be1298ba795d83c09a915e959
metrics:
- type: f1
value: 0.9153
name: F1
- type: precision
value: 0.9116
name: Precision
- type: recall
value: 0.9191
name: Recall
datasets:
- tner/ontonotes5
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 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-roberta-large-ontonotes5")
# 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.