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DistilBERT
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2949
- Accuracy: {'accuracy': 0.9019555317439057}
- F1 Score: {'f1': 0.6131078224101479}
- Precision: {'precision': 0.6697459584295612}
- Recall: {'recall': 0.5653021442495126}
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.2408 | 1.0 | 1626 | 0.2795 | {'accuracy': 0.9049022234128047} | {'f1': 0.5872093023255814} | {'precision': 0.7276657060518732} | {'recall': 0.4922027290448343} |
| 0.1928 | 2.0 | 3252 | 0.2949 | {'accuracy': 0.9019555317439057} | {'f1': 0.6131078224101479} | {'precision': 0.6697459584295612} | {'recall': 0.5653021442495126} |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
