token-classifier/roBERTa-v2

model

This model is a fine-tuned version of roberta-large on the generator dataset. It achieves the following results on the evaluation set:

• Loss: 0.5350
• Precision: 0.5932
• Recall: 0.7371
• F1: 0.6574
• Accuracy: 0.8967

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: 1e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• training_steps: 1000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	0.47	466	0.5513	0.5389	0.7358	0.6222	0.8787
0.4041	1.47	932	0.5179	0.5398	0.7613	0.6317	0.8797
0.3968	2.07	1000	0.5350	0.5932	0.7371	0.6574	0.8967

Framework versions

• Transformers 4.33.2
• Pytorch 2.0.1+cu118
• Datasets 2.14.5
• Tokenizers 0.13.3