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SpeciesFileGroup/
ento-label-deberta

microsoft/deberta-v3-base

quantized

apache-2.0

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ento-label-deberta

DeBERTa-v3 models fine-tuned for NER on insect collection labels. Given a raw label string the model extracts semantic fields as verbatim character spans.

Three sizes are included in this repo: small, base, and large (subdirectories of the same name). ONNX exports are in onnx/small, onnx/base, and onnx/large.

Entity types

Label	Description
`country`	Country name
`state`	State, province, or region
`verbatim_locality`	Locality description
`verbatim_date`	Collection date as written
`verbatim_elevation`	Elevation as written
`verbatim_collectors`	Collector name(s)
`verbatim_habitat`	Habitat description
`verbatim_method`	Collection method
`verbatim_latitude`	Latitude as written
`verbatim_longitude`	Longitude as written

Evaluation results (macro F1 per entity)

Entity	small	base	large
country	0.9695	0.9749	0.9751
state	0.9046	0.9220	0.9212
verbatim_locality	0.8282	0.8499	0.8573
verbatim_date	0.9673	0.9700	0.9693
verbatim_elevation	0.9722	0.9742	0.9739
verbatim_collectors	0.4867	0.5393	0.5311
verbatim_habitat	0.7485	0.7751	0.7930
verbatim_method	0.9123	0.9205	0.9080
verbatim_latitude	0.7154	0.7145	0.6512
verbatim_longitude	0.8552	0.8528	0.7969
macro avg	0.8360	0.8493	0.8377

Usage (PyTorch)

python

from transformers import pipeline

ner = pipeline(
    "token-classification",
    model="SpeciesFileGroup/ento-label-deberta/base",
    aggregation_strategy="simple",
)

results = ner("Sudan, Blue Nile: Abu Hashim, 23-24.XI.1962, coll. Linnavuori")
for r in results:
    print(r["entity_group"], repr(r["word"]))
# country      'Sudan'
# state        'Blue Nile'
# verbatim_locality  'Abu Hashim'
# verbatim_date      '23-24.XI.1962'
# verbatim_collectors 'Linnavuori'

Usage (ONNX / hugot)

ONNX models are compatible with hugot and ONNX Runtime. Load from onnx/small, onnx/base, or onnx/large.

Training

Fine-tuned for 5 epochs with the HuggingFace Trainer. Hyperparameters:

Parameter	small / base	large
Learning rate	5e-6	2e-6
Batch size	16	16
LR scheduler	linear	linear
Warmup ratio	0.06	0.06
Weight decay	0.01	0.01
Max seq length	128	128

Training data: ~22 000 insect collection label strings with character-span annotations for the 10 entity types above.