---
license: apache-2.0
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
pipeline_tag: token-classification
widget:
- text: "Amelia Earthart voló su Lockheed Vega 5B monomotor a través del Océano Atlántico hasta París ."
  example_title: "Spanish"
- text: "Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris ."
  example_title: "English"
- text: "Amelia Earthart a fait voler son monomoteur Lockheed Vega 5B à travers l'ocean Atlantique jusqu'à Paris ."
  example_title: "French"
- text: "Amelia Earthart flog mit ihrer einmotorigen Lockheed Vega 5B über den Atlantik nach Paris ."
  example_title: "German"
- text: "Амелия Эртхарт перелетела на своем одномоторном самолете Lockheed Vega 5B через Атлантический океан в Париж ."
  example_title: "Russian"
- text: "Amelia Earthart vloog met haar één-motorige Lockheed Vega 5B over de Atlantische Oceaan naar Parijs ."
  example_title: "Dutch"
- text: "Amelia Earthart przeleciała swoim jednosilnikowym samolotem Lockheed Vega 5B przez Ocean Atlantycki do Paryża ."
  example_title: "Polish"
- text: "Amelia Earthart flaug eins hreyfils Lockheed Vega 5B yfir Atlantshafið til Parísar ."
  example_title: "Icelandic"
- text: "Η Amelia Earthart πέταξε το μονοκινητήριο Lockheed Vega 5B της πέρα ​​από τον Ατλαντικό Ωκεανό στο Παρίσι ."
  example_title: "Greek"
model-index:
  - name: SpanMarker w. xlm-roberta-base on MultiNERD by Tom Aarsen
    results:
      - task:
          type: token-classification
          name: Named Entity Recognition
        dataset:
          type: Babelscape/multinerd
          name: MultiNERD
          split: test
          revision: 2814b78e7af4b5a1f1886fe7ad49632de4d9dd25
        metrics:
          - type: f1
            value: 0.91314
            name: F1
          - type: precision
            value: 0.91994
            name: Precision
          - type: recall
            value: 0.90643
            name: Recall
datasets:
  - Babelscape/multinerd
language:
  - multilingual
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-base](https://huggingface.co/xlm-roberta-base) as the underlying encoder. See [train.py](train.py) for the training script.

## Metrics

| **Language** | **F1** | **Precision** | **Recall** |
|--------------|--------|---------------|------------|
| all          | 91.31  | 91.99         | 90.64      |
| **de**       | 93.77  | 93.56         | 93.87      |
| **en**       | 94.55  | 94.01         | 95.10      |
| **es**       | 90.82  | 92.58         | 89.13      |
| **fr**       | 90.90  | 93.23         | 88.68      |
| **it**       | 93.40  | 90.23         | 92.60      |
| **nl**       | 92.47  | 93.61         | 91.36      |
| **pl**       | 91.66  | 92.51         | 90.81      |
| **pt**       | 91.73  | 93.29         | 90.22      |
| **ru**       | 92.64  | 92.37         | 92.91      |
| **zh**       | 82.38  | 83.23         | 81.55      |

## 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-base-multinerd")
# 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.