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bert-ner
Shyam-duba/bert-ner
bert-ner is a token classification model from Shyam-duba. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Mar 21, 2026
- Updated
- Mar 21, 2026
- SHA
- 4dc9b58a
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Base models
Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
bert-ner
This model is a fine-tuned version of bert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0551
- Precision: 0.9376
- Recall: 0.9482
- F1: 0.9429
- Accuracy: 0.9868
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.2227 | 1.0 | 878 | 0.0649 | 0.9161 | 0.9269 | 0.9215 | 0.9823 |
| 0.0465 | 2.0 | 1756 | 0.0561 | 0.9319 | 0.9436 | 0.9377 | 0.9858 |
| 0.0268 | 3.0 | 2634 | 0.0551 | 0.9376 | 0.9482 | 0.9429 | 0.9868 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 2.18.0
- Tokenizers 0.22.2
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Mar 21, 2026
- Updated
- Mar 21, 2026