t5_small_language_Classification

This model is a fine-tuned version of yigagilbert/t5_efficient_small_language_ID on the generator dataset. It achieves the following results on the evaluation set:

Model description

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The following hyperparameters were used during training:

learning_rate: 0.0005
train_batch_size: 64
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 128
optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine_with_restarts
lr_scheduler_warmup_steps: 1000
training_steps: 60000

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.6453	0.0083	500	1.7792	0.5575	0.6272	0.5575	0.5283
0.3701	0.0167	1000	2.8566	0.4925	0.6309	0.4925	0.4427
0.3602	0.025	1500	3.4108	0.4331	0.6188	0.4331	0.3903
0.3573	0.0333	2000	1.9821	0.5855	0.6303	0.5855	0.5419
0.4229	0.0417	2500	1.9248	0.6071	0.6712	0.6071	0.5731
0.2156	0.05	3000	2.6673	0.5217	0.6906	0.5217	0.4851
0.3752	0.0583	3500	1.9381	0.5984	0.6682	0.5984	0.5619
0.4996	0.0667	4000	1.5622	0.6266	0.6757	0.6266	0.6022
0.2773	0.075	4500	1.8355	0.6299	0.6892	0.6299	0.5872
0.2815	0.0833	5000	1.7752	0.6423	0.6905	0.6423	0.6034
0.2525	0.0917	5500	1.6552	0.6450	0.6879	0.6450	0.6082
0.2271	0.1	6000	1.6523	0.6575	0.6916	0.6575	0.6278
0.3591	0.1083	6500	1.7169	0.6542	0.6985	0.6542	0.6238
0.2659	0.1167	7000	1.7209	0.6439	0.7090	0.6439	0.6180
0.2337	0.125	7500	1.7631	0.6531	0.7019	0.6531	0.6158