learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Use adamw_torch 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: 300
num_epochs: 6
label_smoothing_factor: 0.1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
1.0529	0.4	250	0.7700	0.8142	0.8169	0.8336	0.8142
0.6938	0.8	500	0.6479	0.8762	0.8776	0.8854	0.8762
0.6216	1.2	750	0.6049	0.901	0.9020	0.9060	0.901
0.5676	1.6	1000	0.6214	0.8978	0.8979	0.9005	0.8978
0.5777	2.0	1250	0.5493	0.9236	0.9241	0.9252	0.9236
0.5118	2.4	1500	0.5507	0.9236	0.9236	0.9243	0.9236
0.4909	2.8	1750	0.5579	0.927	0.9269	0.9272	0.927
0.4595	3.2	2000	0.5508	0.9314	0.9313	0.9313	0.9314
0.4538	3.6	2250	0.5691	0.9244	0.9239	0.9248	0.9244
0.4445	4.0	2500	0.5581	0.9308	0.9308	0.9314	0.9308

Framework versions

Transformers 4.51.3
Pytorch 2.1.2
Datasets 3.5.1
Tokenizers 0.21.1

Training Information:

TrainOutput(global_step=2500, training_loss=0.6116815521240234, metrics={'train_runtime': 805.1081, 'train_samples_per_second': 149.048, 'train_steps_per_second': 4.658, 'total_flos': 2.104945139712e+16, 'train_loss': 0.6116815521240234, 'epoch': 4.0})

Ghazi-nak/6_BiasDetctor_HTW_ANG.KI

6_BiasDetctor_HTW_ANG.KI

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Training Information:

Ghazi-nak/
6_BiasDetctor_HTW_ANG.KI