binary-classification
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.3009
- Accuracy: 0.8968
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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.175 | 1.0 | 4210 | 0.3009 | 0.8968 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
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Dataset used to train autoevaluate/binary-classification
Evaluation results
- Accuracy on glueself-reported0.897
- Accuracy on gluevalidation set verified0.897
- Precision on gluevalidation set verified0.890
- Recall on gluevalidation set verified0.910
- AUC on gluevalidation set verified0.967
- F1 on gluevalidation set verified0.900
- loss on gluevalidation set verified0.301
- matthews_correlation on gluevalidation set verified0.794