README.md

---
language:
- en
license: apache-2.0
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
datasets:
- conll2003
metrics:
- f1
- recall
- precision
pipeline_tag: token-classification
widget:
- text: Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic
    to Paris.
  example_title: Amelia Earhart
base_model: prajjwal1/bert-tiny
model-index:
- name: SpanMarker w. bert-tiny on CoNLL03 by Tom Aarsen
  results:
  - task:
      type: token-classification
      name: Named Entity Recognition
    dataset:
      name: CoNLL03
      type: conll2003
      split: test
      revision: 01ad4ad271976c5258b9ed9b910469a806ff3288
    metrics:
    - type: f1
      value: 0.8093994778067886
      name: F1
    - type: precision
      value: 0.8546048601184398
      name: Precision
    - type: recall
      value: 0.7687362233651727
      name: Recall
---

# 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 [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) as the underlying encoder. 

## Note
This model is primarily used for efficient tests on the [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) GitHub repository.

## 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-bert-tiny-conll03")
# 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.