SpanMarker with numind/generic-entity_recognition-v1 on FewNERD

This is a SpanMarker model trained on the FewNERD dataset that can be used for Named Entity Recognition. This SpanMarker model uses numind/generic-entity_recognition_NER-v1 as the underlying encoder.

Model Details

Model Description

• Model Type: SpanMarker
• Encoder: numind/generic-entity_recognition_NER-v1
• Maximum Sequence Length: 256 tokens
• Maximum Entity Length: 8 words
• Training Dataset: FewNERD
• Language: en
• License: cc-by-sa-4.0

Model Sources

• Repository: SpanMarker on GitHub
• Thesis: SpanMarker For Named Entity Recognition

Model Labels

Uses

Direct Use for Inference

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("guishe/span-marker-generic-ner-v1-fewnerd-fine-super")
# Run inference
entities = model.predict("Most of the Steven Seagal movie \"Under Siege \"(co-starring Tommy Lee Jones) was filmed on the, which is docked on Mobile Bay at Battleship Memorial Park and open to the public.")

Downstream Use

You can finetune this model on your own dataset.

Training Details

Training Set Metrics

Training Hyperparameters

• learning_rate: 1e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 3

Training Results

Framework Versions

• Python: 3.10.8
• SpanMarker: 1.5.0
• Transformers: 4.28.0
• PyTorch: 1.13.1+cu117
• Datasets: 2.14.4
• Tokenizers: 0.13.3

Citation

BibTeX

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

Base model

numind/NuNER-v0.1

(4)

This collections gathers several NER models. Either fine-tuned versions for specific tasks or generic backbone models ready to be fine-tuned. • 12 items • Updated Feb 5, 2025 • 1