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trungpq/
rlcc-taste-upsample_replacement-absa-max

Name: rlcc-taste-upsample_replacement-absa-max
Availability: InStock

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rlcc-taste-upsample_replacement-absa-max

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 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: 90
num_epochs: 25

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	Precision Macro	Recall Macro	Total Tf
1.0972	1.0	91	1.0942	0.4146	0.4391	0.4352	0.5286	[170, 240, 990, 240]
0.8855	2.0	182	1.0312	0.5659	0.5548	0.5485	0.5883	[232, 178, 1052, 178]
0.722	3.0	273	1.0728	0.5366	0.5509	0.5488	0.5696	[220, 190, 1040, 190]
0.6273	4.0	364	1.1465	0.5659	0.5568	0.5071	0.6596	[232, 178, 1052, 178]
0.5782	5.0	455	1.2204	0.5878	0.6265	0.6269	0.6555	[241, 169, 1061, 169]
0.5685	6.0	546	1.2278	0.6171	0.6357	0.7025	0.6389	[253, 157, 1073, 157]
0.5106	7.0	637	1.2278	0.6366	0.6815	0.6773	0.6898	[261, 149, 1081, 149]
0.4165	8.0	728	1.2367	0.6463	0.6916	0.6887	0.7017	[265, 145, 1085, 145]
0.3578	9.0	819	1.2293	0.6415	0.6873	0.6890	0.6864	[263, 147, 1083, 147]
0.284	10.0	910	1.2811	0.6561	0.7006	0.6986	0.7034	[269, 141, 1089, 141]
0.2218	11.0	1001	1.4029	0.6439	0.6907	0.6922	0.6896	[264, 146, 1084, 146]
0.1906	12.0	1092	1.5355	0.6463	0.6918	0.6907	0.6933	[265, 145, 1085, 145]
0.181	13.0	1183	1.5934	0.6512	0.6968	0.6989	0.6989	[267, 143, 1087, 143]
0.1284	14.0	1274	1.6647	0.6512	0.6965	0.6995	0.6946	[267, 143, 1087, 143]
0.1186	15.0	1365	1.6405	0.6780	0.7197	0.7258	0.7161	[278, 132, 1098, 132]
0.1266	16.0	1456	1.7077	0.6634	0.7074	0.7191	0.7028	[272, 138, 1092, 138]
0.1077	17.0	1547	1.7719	0.6634	0.7063	0.7127	0.7028	[272, 138, 1092, 138]
0.1029	18.0	1638	1.8189	0.6488	0.6944	0.7006	0.6934	[266, 144, 1086, 144]
0.0863	19.0	1729	1.9092	0.6512	0.6957	0.7044	0.6907	[267, 143, 1087, 143]
0.0748	20.0	1820	1.8930	0.6585	0.7021	0.7089	0.6981	[270, 140, 1090, 140]