Training parameters:

```
model_args = ClassificationArgs()
model_args.max_seq_length = 512
model_args.train_batch_size = 12
model_args.eval_batch_size = 12
model_args.num_train_epochs = 5
model_args.evaluate_during_training = False
model_args.learning_rate = 1e-5
model_args.use_multiprocessing = False
model_args.fp16 = False
model_args.save_steps = -1
model_args.save_eval_checkpoints = False
model_args.no_cache = True
model_args.reprocess_input_data = True
model_args.overwrite_output_dir = True
```

Evaluation on BoolQ Test Set:

|              | Precision | Recall | F1-score |
|:------------:|:---------:|:------:|:--------:|
|       0      |    0.82   |  0.80  |   0.81   |
|       1      |    0.88   |  0.89  |   0.88   |
|   accuracy   |           |        |   0.86   |
|   macro avg  |    0.85   |  0.84  |   0.85   |
| weighted avg |    0.86   |  0.86  |   0.86   |

ROC AUC Score: 0.844
{}
transformers pytorch roberta text-classification autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us 

Training parameters:


Evaluation on BoolQ Test Set:



ROC AUC Score: 0.844



TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us 
