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--- |
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language: |
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- en |
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license: apache-2.0 |
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library_name: span-marker |
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tags: |
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- span-marker |
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- token-classification |
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- ner |
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- named-entity-recognition |
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datasets: |
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- conll2003 |
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metrics: |
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- f1 |
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- recall |
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- precision |
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pipeline_tag: token-classification |
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widget: |
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- text: Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic |
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to Paris. |
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example_title: Amelia Earhart |
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base_model: xlm-roberta-large |
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model-index: |
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- name: SpanMarker w. xlm-roberta-large on CoNLL03 by Tom Aarsen |
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results: |
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- task: |
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type: token-classification |
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name: Named Entity Recognition |
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dataset: |
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name: CoNLL03 |
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type: conll2003 |
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split: test |
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revision: 01ad4ad271976c5258b9ed9b910469a806ff3288 |
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metrics: |
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- type: f1 |
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value: 0.9307 |
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name: F1 |
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- type: precision |
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value: 0.9264 |
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name: Precision |
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- type: recall |
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value: 0.935 |
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name: Recall |
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--- |
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# SpanMarker for Named Entity Recognition |
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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. |
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## Usage |
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To use this model for inference, first install the `span_marker` library: |
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```bash |
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pip install span_marker |
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``` |
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You can then run inference with this model like so: |
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```python |
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from span_marker import SpanMarkerModel |
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# Download from the 🤗 Hub |
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model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-xlm-roberta-large-conll03") |
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# Run inference |
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entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.") |
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``` |
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### Limitations |
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**Warning**: This model works best when punctuation is separated from the prior words, so |
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```python |
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# ✅ |
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model.predict("He plays J. Robert Oppenheimer , an American theoretical physicist .") |
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# ❌ |
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model.predict("He plays J. Robert Oppenheimer, an American theoretical physicist.") |
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# You can also supply a list of words directly: ✅ |
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model.predict(["He", "plays", "J.", "Robert", "Oppenheimer", ",", "an", "American", "theoretical", "physicist", "."]) |
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``` |
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The same may be beneficial for some languages, such as splitting `"l'ocean Atlantique"` into `"l' ocean Atlantique"`. |
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See the [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) repository for documentation and additional information on this library. |
