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mmbert-small-ner-ra
clincolnoz/mmbert-small-ner-ra
mmbert-small-ner-ra is a token classification model from clincolnoz. Use it when you need labels on individual words, such as names. It is set up for transformers.
Token classification (PER / ORG / LOC, IOB2) fine-tuned from jhu-clsp/mmBERT-small on a 32-language news NER dataset.
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At a glance
- Task
- Token Classification
- Library
- transformers
- Model type
- modernbert
- Access
- Public
- Created
- Sep 23, 2026
- Updated
- Sep 23, 2026
- SHA
- 4f839e8e
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Base models
Model card
From the Hugging Face model README
mmBERT-small fine-tuned for multilingual NER
Token classification (PER / ORG / LOC, IOB2) fine-tuned from
jhu-clsp/mmBERT-small on a
32-language news NER dataset.
Training
- lr 8e-5, up to 10 epochs with early stopping (patience 3) on validation F1, warmup 0.1, weight decay 0.01, max length 256
- Training labels denoised by masking contradictory repeat mentions from the loss
- Uniform weight average of 3 seeds
Validation results
Leak-free, stratified validation split, seqeval micro scores at 256-token truncation.
| metric | value |
|---|---|
| F1 | 0.7375 |
| precision | 0.6583 |
| recall | 0.8383 |
Per class F1: LOC 0.7816, ORG 0.6955, PER 0.8024.
Usage
from transformers import pipeline
ner = pipeline("token-classification", model="clincolnoz/mmbert-small-ner-ra", aggregation_strategy="simple")
ner("Angela Merkel met executives from Siemens in Munich.")
- Task
- Token Classification
- Library
- transformers
- Type
- modernbert
- Created
- Sep 23, 2026
- Updated
- Sep 23, 2026