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model1-thesis
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: 1.7088
- Precision: 0.0487
- Recall: 0.0532
- F1: 0.0509
- Accuracy: 0.3175
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: 3
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 12 | 1.7903 | 0.0152 | 0.0169 | 0.0160 | 0.2535 |
No log | 2.0 | 24 | 1.7359 | 0.0458 | 0.0464 | 0.0461 | 0.2944 |
No log | 3.0 | 36 | 1.7088 | 0.0487 | 0.0532 | 0.0509 | 0.3175 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2