learning_rate: 1.5e-05
train_batch_size: 8
eval_batch_size: 8
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: cosine
lr_scheduler_warmup_ratio: 0.2
num_epochs: 6
mixed_precision_training: Native AMP

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
2.1051	1.0	10477	1.3030	0.6090	0.6822	0.6090	0.6244
1.1651	2.0	20954	1.1967	0.6498	0.6994	0.6498	0.6562
0.7921	3.0	31431	1.2176	0.6773	0.7160	0.6773	0.6870
0.4773	4.0	41908	1.3870	0.7009	0.7192	0.7009	0.7052
0.2639	5.0	52385	1.6282	0.7073	0.7166	0.7073	0.7102
0.1675	6.0	62862	1.7318	0.7101	0.7156	0.7101	0.7118

AbrarAbhinaya/BERTscenario4-news-classifier

BERTscenario4-news-classifier