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bert-base-uncased-finetuned-scientific-eval
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1920
- Precision: 0.6240
- Recall: 0.7066
- F1: 0.6627
- Accuracy: 0.9502
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 71 | 0.3069 | 0.4263 | 0.3375 | 0.3768 | 0.9212 |
No log | 2.0 | 142 | 0.2241 | 0.4883 | 0.5899 | 0.5343 | 0.9359 |
No log | 3.0 | 213 | 0.1962 | 0.5745 | 0.6688 | 0.6181 | 0.9441 |
No log | 4.0 | 284 | 0.1920 | 0.6240 | 0.7066 | 0.6627 | 0.9502 |
No log | 5.0 | 355 | 0.2107 | 0.5694 | 0.7634 | 0.6523 | 0.9465 |
No log | 6.0 | 426 | 0.2070 | 0.6286 | 0.7634 | 0.6895 | 0.9514 |
No log | 7.0 | 497 | 0.2129 | 0.6193 | 0.7697 | 0.6864 | 0.9499 |
0.1579 | 8.0 | 568 | 0.2269 | 0.6496 | 0.7603 | 0.7006 | 0.9529 |
0.1579 | 9.0 | 639 | 0.2274 | 0.6366 | 0.7571 | 0.6916 | 0.9519 |
0.1579 | 10.0 | 710 | 0.2285 | 0.6486 | 0.7571 | 0.6987 | 0.9522 |
Framework versions
- Transformers 4.27.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3