---
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
- generated_from_span_marker_trainer
metrics:
- precision
- recall
- f1
widget: []
pipeline_tag: token-classification
language:
- ar
---

# SpanMarker

This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition.

## Model Details

### Model Description
- **Model Type:** SpanMarker
<!-- - **Encoder:** [Unknown](https://huggingface.co./unknown) -->
- **Maximum Sequence Length:** 512 tokens
- **Maximum Entity Length:** 150 words
<!-- - **Training Dataset:** [Unknown](https://huggingface.co./datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Repository:** [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER)
- **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf)

## Uses

### Direct Use for Inference

```python
from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("iahlt/xlm-roberta-base-ar-ner-flat")
entities = model.predict("None")
print(entities)
```

## Training Details

### Framework Versions
- Python: 3.10.12
- SpanMarker: 1.5.0
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.1

## Citation

### BibTeX

```
@software{Aarsen_SpanMarker,
    author = {Aarsen, Tom},
    license = {Apache-2.0},
    title = {{SpanMarker for Named Entity Recognition}},
    url = {https://github.com/tomaarsen/SpanMarkerNER}
}
```