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: 0.0618
- Precision: 0.9377
- Recall: 0.9517
- F1: 0.9446
- Accuracy: 0.9863
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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.0701 | 1.0 | 1756 | 0.0678 | 0.9101 | 0.9349 | 0.9223 | 0.9814 |
0.033 | 2.0 | 3512 | 0.0646 | 0.9353 | 0.9505 | 0.9428 | 0.9863 |
0.0213 | 3.0 | 5268 | 0.0618 | 0.9377 | 0.9517 | 0.9446 | 0.9863 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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