RuHalluBERT-base-v4

This model is a fine-tuned version of deepvk/RuModernBERT-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.9101
• F1 Macro: 0.7177
• F1 Class1: 0.6720
• F1 Class0: 0.7634
• Accuracy: 0.7251
• Precision Macro: 0.7252
• Recall Macro: 0.7160

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: 2
• eval_batch_size: 2
• seed: 42
• distributed_type: multi-GPU
• gradient_accumulation_steps: 16
• total_train_batch_size: 32
• 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: cosine
• lr_scheduler_warmup_steps: 0.06
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Macro	F1 Class1	F1 Class0	Accuracy	Precision Macro	Recall Macro
11.2782	1.0	113	0.6490	0.6090	0.5076	0.7105	0.6353	0.6385	0.6155
10.4259	2.0	226	0.6731	0.5534	0.6783	0.4286	0.5884	0.7154	0.6228
8.9919	3.0	339	0.5547	0.7192	0.6884	0.75	0.7226	0.7197	0.7188
7.0874	4.0	452	0.5604	0.7349	0.6963	0.7734	0.7405	0.7396	0.7333
5.1057	5.0	565	0.6218	0.7091	0.7059	0.7124	0.7092	0.7144	0.7153
4.0620	6.0	678	0.6958	0.7181	0.7175	0.7188	0.7181	0.7250	0.7252
2.7632	7.0	791	0.7622	0.7370	0.6982	0.7758	0.7427	0.7421	0.7353
2.2915	8.0	904	0.8325	0.7164	0.6942	0.7386	0.7181	0.7160	0.7175
1.9114	9.0	1017	0.8926	0.7336	0.7079	0.7592	0.7360	0.7334	0.7338
1.2978	10.0	1130	0.9135	0.7269	0.7012	0.7526	0.7293	0.7266	0.7272

Framework versions

• Transformers 5.8.1
• Pytorch 2.11.0+cu130
• Datasets 4.8.5
• Tokenizers 0.22.2