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 CoNLL++ with document-level context by
Tom Aarsen
results:
- task:
type: token-classification
name: Named Entity Recognition
dataset:
type: conllpp
name: CoNLL++ w. document context
split: test
revision: 3e6012875a688903477cca9bf1ba644e65480bd6
metrics:
- type: f1
value: 0.9554
name: F1
- type: precision
value: 0.96
name: Precision
- type: recall
value: 0.9509
name: Recall
datasets:
- conllpp
- tomaarsen/conllpp
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-conllpp-doc-context")
# 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.