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bert-finetuned-ner
mikavh/bert-finetuned-ner
bert-finetuned-ner is a token classification model from mikavh. 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 16, 2025
- Updated
- Nov 16, 2025
- SHA
- 56ab861c
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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 an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2360
- Precision: 0.4943
- Recall: 0.6855
- F1: 0.5744
- Accuracy: 0.9450
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 | 252 | 0.2271 | 0.4476 | 0.6541 | 0.5315 | 0.9412 |
| 0.1973 | 2.0 | 504 | 0.2273 | 0.4829 | 0.6939 | 0.5695 | 0.9426 |
| 0.1973 | 3.0 | 756 | 0.2360 | 0.4943 | 0.6855 | 0.5744 | 0.9450 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.6.0+cpu
- Datasets 4.4.1
- Tokenizers 0.22.1
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Nov 16, 2025
- Updated
- Nov 16, 2025