README.md

---
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"
model-index:
  - name: SpanMarker w. roberta-large on finegrained, supervised FewNERD by Tom Aarsen
    results:
      - task:
          type: token-classification
          name: Named Entity Recognition
        dataset:
          type: DFKI-SLT/few-nerd
          name: finegrained, supervised FewNERD
          config: supervised
          split: test
          revision: 2e3e727c63604fbfa2ff4cc5055359c84fe5ef2c
        metrics:
          - type: f1
            value: 0.7103
            name: F1
          - type: precision
            value: 0.7136
            name: Precision
          - type: recall
            value: 0.7070
            name: Recall
datasets:
  - DFKI-SLT/few-nerd
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. 

## 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-fewnerd-fine-super")
# 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.