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bert-finetuned-ner
Olaperi/bert-finetuned-ner
bert-finetuned-ner is a token classification model from Olaperi. 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 13, 2024
- Updated
- Nov 13, 2024
- SHA
- 0b80055e
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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: 0.0623
- Precision: 0.9342
- Recall: 0.9507
- F1: 0.9424
- Accuracy: 0.9860
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 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 |
|---|---|---|---|---|---|---|---|
| 0.0772 | 1.0 | 1756 | 0.0688 | 0.9086 | 0.9330 | 0.9206 | 0.9815 |
| 0.0345 | 2.0 | 3512 | 0.0692 | 0.9291 | 0.9461 | 0.9375 | 0.9844 |
| 0.0212 | 3.0 | 5268 | 0.0623 | 0.9342 | 0.9507 | 0.9424 | 0.9860 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Tokenizers 0.20.3
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Nov 13, 2024
- Updated
- Nov 13, 2024