---
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. bert-base-cased 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: 0.7053
      name: F1
    - type: precision
      value: 0.7101
      name: Precision
    - type: recall
      value: 0.7005
      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 [bert-base-cased](https://huggingface.co/bert-base-cased) 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-bert-base-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.