electra-base-discriminator-finetuned-detests-wandb24

This model is a fine-tuned version of google/electra-base-discriminator on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.4457
• Accuracy: 0.7741
• F1-score: 0.6965
• Precision: 0.6879
• Recall: 0.7092
• Auc: 0.7092

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: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• 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	Auc
0.4719	1.0	77	0.4698	0.7971	0.6380	0.7159	0.6199	0.6199
0.4737	2.0	154	0.4457	0.7741	0.6965	0.6879	0.7092	0.7092

Framework versions

• Transformers 4.37.2
• Pytorch 2.1.0+cu121
• Datasets 2.17.0
• Tokenizers 0.15.1