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---
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. |