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---
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](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition. In particular, this SpanMarker model uses [roberta-large](https://huggingface.co/roberta-large) as the underlying encoder. See [train.py](train.py) for the training script.
## Usage
To use this model for inference, first install the `span_marker` library:
```bash
pip install span_marker
```
You can then run inference with this model like so:
```python
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.")
```
### Limitations
**Warning**: This model works best when punctuation is separated from the prior words, so
```python
# ✅
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](https://github.com/tomaarsen/SpanMarkerNER) repository for documentation and additional information on this library. |