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bert-finetuned-ner
v-rezan/bert-finetuned-ner
bert-finetuned-ner is a token classification model from v-rezan. 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
- Nov 11, 2025
- Updated
- Nov 12, 2025
- SHA
- 4fc06f48
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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-finetuned-ner
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2324
- Precision: 0.4781
- Recall: 0.6749
- F1: 0.5597
- Accuracy: 0.9427
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 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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 249 | 0.2125 | 0.4488 | 0.6183 | 0.5201 | 0.9410 |
| No log | 2.0 | 498 | 0.2116 | 0.4984 | 0.6502 | 0.5643 | 0.9459 |
| 0.1858 | 3.0 | 747 | 0.2324 | 0.4781 | 0.6749 | 0.5597 | 0.9427 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.9.0+cu126
- Datasets 3.3.2
- Tokenizers 0.21.0
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Nov 11, 2025
- Updated
- Nov 12, 2025