results_v2.2

This model is a fine-tuned version of haval995/roberta-large-causal-hallucination-detector_V_2.1 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.4622
• Accuracy: 0.8696
• F1 Macro: 0.8460
• F1 Supported: 0.7857
• Recall Supported: 1.0
• F1 Hallucination: 0.9062
• Recall Hallucination: 0.8286

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: 16
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 0.1
• num_epochs: 6
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	F1 Supported	Recall Supported	F1 Hallucination	Recall Hallucination
0.6675	1.0	54	0.3172	0.9338	0.9334	0.9286	0.9848	0.9383	0.8941
0.5336	2.0	108	0.3151	0.9404	0.9395	0.9323	0.9394	0.9467	0.9412
0.5047	3.0	162	0.3452	0.9470	0.9466	0.9420	0.9848	0.9512	0.9176
0.4754	4.0	216	0.3812	0.9205	0.9202	0.9155	0.9848	0.925	0.8706
0.4676	5.0	270	0.3236	0.9470	0.9465	0.9412	0.9697	0.9518	0.9294

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.2