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model1-thesis-7
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: 0.7392
- Precision: 0.4335
- Recall: 0.5906
- F1: 0.5
- Accuracy: 0.8047
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: 5e-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: 5
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 63 | 0.7911 | 0.3484 | 0.4746 | 0.4018 | 0.7399 |
No log | 2.0 | 126 | 0.6398 | 0.4073 | 0.5254 | 0.4589 | 0.7918 |
No log | 3.0 | 189 | 0.7744 | 0.3776 | 0.5362 | 0.4431 | 0.7825 |
No log | 4.0 | 252 | 0.7103 | 0.4301 | 0.5906 | 0.4977 | 0.7983 |
No log | 5.0 | 315 | 0.7392 | 0.4335 | 0.5906 | 0.5 | 0.8047 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2