File size: 2,196 Bytes
d6e0638 5ed7dee d6e0638 ffd8a89 d6e0638 5ed7dee d6e0638 b7999fa 5ed7dee cba2bec 5ed7dee d6e0638 20d4d03 d6e0638 144fdd6 d6e0638 ffd8a89 d6e0638 ffd8a89 cba2bec d6e0638 f8d08ee d6e0638 ffd8a89 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
---
language:
- en
license: apache-2.0
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
datasets:
- DFKI-SLT/few-nerd
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
- text: Leonardo di ser Piero da Vinci painted the Mona Lisa based on Italian noblewoman
Lisa del Giocondo.
example_title: Leonardo da Vinci
base_model: prajjwal1/bert-tiny
model-index:
- name: SpanMarker w. bert-base-cased on coarsegrained, supervised FewNERD by Tom
Aarsen
results:
- task:
type: token-classification
name: Named Entity Recognition
dataset:
name: coarsegrained, supervised FewNERD
type: DFKI-SLT/few-nerd
config: supervised
split: test
revision: 2e3e727c63604fbfa2ff4cc5055359c84fe5ef2c
metrics:
- type: f1
value: 0.7081
name: F1
- type: precision
value: 0.7378
name: Precision
- type: recall
value: 0.6808
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-fewnerd-coarse-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. |