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bert-finetuned-ner
This model is a fine-tuned version of bert-large-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2253
- Precision: 0.7174
- Recall: 0.825
- F1: 0.7674
- Accuracy: 0.9314
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: 4
- 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
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 126 | 0.3449 | 0.5165 | 0.5875 | 0.5497 | 0.8914 |
No log | 2.0 | 252 | 0.2569 | 0.6566 | 0.8125 | 0.7263 | 0.9230 |
No log | 3.0 | 378 | 0.2253 | 0.7174 | 0.825 | 0.7674 | 0.9314 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1