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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
model-index:
- name: >-
SpanMarker w. xlm-roberta-large on CoNLL03 with document-level context by Tom Aarsen
results:
- task:
type: token-classification
name: Named Entity Recognition
dataset:
type: conll2003
name: CoNLL03 w. document context
split: test
revision: 01ad4ad271976c5258b9ed9b910469a806ff3288
metrics:
- type: f1
value: 0.9442
name: F1
- type: precision
value: 0.9411
name: Precision
- type: recall
value: 0.9473
name: Recall
datasets:
- conll2003
- tomaarsen/conll2003
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 [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) as the underlying encoder. See [train.py](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](https://tomaarsen.github.io/SpanMarkerNER/api/span_marker.modeling.html#span_marker.modeling.SpanMarkerModel.predict) of the `model.predict` method for more information.
## 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-xlm-roberta-large-conll03-doc-context")
# Run inference
entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.")
```
See the [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) repository for documentation and additional information on this library. |