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trungpq/
rlcc-new-palate-upsample_replacement-absa-min

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rlcc-new-palate-upsample_replacement-absa-min

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

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The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH 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: 21
num_epochs: 25

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	Precision Macro	Recall Macro	F1 Micro	Precision Micro	Recall Micro	Total Tf
1.1703	1.0	22	1.1184	0.3371	0.2551	0.2415	0.3547	0.3371	0.3371	0.3371	[60, 118, 238, 118]
1.0381	2.0	44	1.0635	0.4101	0.4060	0.4081	0.4154	0.4101	0.4101	0.4101	[73, 105, 251, 105]
0.829	3.0	66	1.0048	0.4944	0.4798	0.4809	0.5040	0.4944	0.4944	0.4944	[88, 90, 266, 90]
0.6768	4.0	88	1.0591	0.5169	0.5128	0.5133	0.5174	0.5169	0.5169	0.5169	[92, 86, 270, 86]
0.5319	5.0	110	1.0832	0.5225	0.5277	0.5393	0.5233	0.5225	0.5225	0.5225	[93, 85, 271, 85]
0.4617	6.0	132	1.1186	0.5281	0.5342	0.5527	0.5291	0.5281	0.5281	0.5281	[94, 84, 272, 84]
0.3559	7.0	154	1.2642	0.5169	0.5181	0.5789	0.5127	0.5169	0.5169	0.5169	[92, 86, 270, 86]
0.2619	8.0	176	1.3052	0.5506	0.5527	0.6115	0.5460	0.5506	0.5506	0.5506	[98, 80, 276, 80]
0.1902	9.0	198	1.3957	0.5337	0.5356	0.5850	0.5309	0.5337	0.5337	0.5337	[95, 83, 273, 83]
0.123	10.0	220	1.4476	0.5337	0.5391	0.5794	0.5312	0.5337	0.5337	0.5337	[95, 83, 273, 83]
0.098	11.0	242	1.5094	0.5562	0.5593	0.6167	0.5544	0.5562	0.5562	0.5562	[99, 79, 277, 79]
0.0866	12.0	264	1.5920	0.5506	0.5483	0.6210	0.5457	0.5506	0.5506	0.5506	[98, 80, 276, 80]
0.1317	13.0	286	1.5906	0.5449	0.5475	0.6087	0.5423	0.5449	0.5449	0.5449	[97, 81, 275, 81]
0.0705	14.0	308	1.6357	0.5449	0.5503	0.5979	0.5431	0.5449	0.5449	0.5449	[97, 81, 275, 81]
0.0464	15.0	330	1.6739	0.5393	0.5388	0.5994	0.5344	0.5393	0.5393	0.5393	[96, 82, 274, 82]
0.0585	16.0	352	1.7286	0.5337	0.5388	0.5784	0.5312	0.5337	0.5337	0.5337	[95, 83, 273, 83]