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bert_finetune_ner
sawarni99/bert_finetune_ner
bert_finetune_ner is a token classification model from sawarni99. 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
- Jun 6, 2026
- Updated
- Jun 7, 2026
- SHA
- 0470e56d
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Base models
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_finetune_ner
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3157
- Precision: 0.8163
- Recall: 0.8456
- F1: 0.8307
- Accuracy: 0.9275
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: 8
- eval_batch_size: 8
- 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 |
|---|---|---|---|---|---|---|---|
| 0.2980 | 1.0 | 2500 | 0.2650 | 0.7885 | 0.8308 | 0.8091 | 0.9212 |
| 0.2006 | 2.0 | 5000 | 0.2736 | 0.8064 | 0.8347 | 0.8203 | 0.9254 |
| 0.1389 | 3.0 | 7500 | 0.3157 | 0.8163 | 0.8456 | 0.8307 | 0.9275 |
Framework versions
- Transformers 5.9.0
- Pytorch 2.12.0
- Datasets 4.8.5
- Tokenizers 0.22.2
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Jun 6, 2026
- Updated
- Jun 7, 2026