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bert-finetuned-ner
Spike20/bert-finetuned-ner
bert-finetuned-ner is a token classification model from Spike20. 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
- Jul 30, 2025
- Updated
- Jul 30, 2025
- SHA
- d55fe587
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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-finetuned-ner
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0569
- Precision: 0.9449
- Recall: 0.9524
- F1: 0.9486
- Accuracy: 0.9912
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 |
|---|---|---|---|---|---|---|---|
| 0.0059 | 1.0 | 1756 | 0.0572 | 0.9402 | 0.9472 | 0.9437 | 0.9902 |
| 0.0094 | 2.0 | 3512 | 0.0597 | 0.9425 | 0.9467 | 0.9446 | 0.9905 |
| 0.004 | 3.0 | 5268 | 0.0569 | 0.9449 | 0.9524 | 0.9486 | 0.9912 |
Framework versions
- Transformers 4.54.1
- Pytorch 2.6.0+cu124
- Datasets 2.16.0
- Tokenizers 0.21.2
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Jul 30, 2025
- Updated
- Jul 30, 2025