B001_cleaned

This model is a fine-tuned version of distilbert-base-uncased on the essay_dataset dataset. It achieves the following results on the evaluation set:

• Loss: 2.2117
• Accuracy: {'accuracy': 0.10526315789473684}
• Precision: {'precision': 0.013157894736842105}
• Recall: {'recall': 0.125}
• F1: {'f1': 0.02380952380952381}

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: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	1.0	13	2.2061	{'accuracy': 0.10526315789473684}	{'precision': 0.013157894736842105}	{'recall': 0.125}	{'f1': 0.02380952380952381}
No log	2.0	26	2.2050	{'accuracy': 0.10526315789473684}	{'precision': 0.013157894736842105}	{'recall': 0.125}	{'f1': 0.02380952380952381}
No log	3.0	39	2.2045	{'accuracy': 0.10526315789473684}	{'precision': 0.013157894736842105}	{'recall': 0.125}	{'f1': 0.02380952380952381}
No log	4.0	52	2.2117	{'accuracy': 0.10526315789473684}	{'precision': 0.013157894736842105}	{'recall': 0.125}	{'f1': 0.02380952380952381}

Framework versions

• Transformers 4.37.1
• Pytorch 2.1.2+cu121
• Datasets 2.16.1
• Tokenizers 0.15.1