Regression_bert_NOaug_CustomLoss

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

Model description

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The following hyperparameters were used during training:

optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 1e-04, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
training_precision: float32

Train Loss	Train Mae	Train Mse	Train R2-score	Validation Loss	Validation Mae	Validation Mse	Validation R2-score	Epoch
0.1477	0.5158	0.3587	0.8489	0.1118	0.5348	0.3366	0.8997	0
0.1280	0.4634	0.2930	0.8414	0.1375	0.4847	0.3121	0.8873	1
0.1232	0.4331	0.2728	-0.3855	0.1453	0.5454	0.4140	0.8773	2
0.0862	0.3752	0.2042	0.8843	0.1683	0.4117	0.2940	0.8728	3
0.0827	0.3573	0.1824	0.9046	0.1383	0.3792	0.2434	0.8940	4
0.0701	0.4034	0.2084	0.8164	0.1313	0.4766	0.3297	0.8879	5
0.0473	0.2988	0.1245	0.8744	0.1544	0.4001	0.2930	0.8780	6
0.0370	0.2501	0.0887	0.8672	0.1464	0.4236	0.3019	0.8809	7
0.0346	0.3122	0.1224	0.9196	0.1296	0.4837	0.3147	0.8885	8
0.0303	0.2493	0.0864	0.9624	0.1399	0.4292	0.2975	0.8876	9
0.0312	0.2527	0.0862	0.9426	0.1436	0.3984	0.2722	0.8876	10
0.0301	0.2160	0.0657	0.6312	0.1479	0.3819	0.2836	0.8849	11
0.0275	0.2286	0.0712	0.9543	0.1473	0.3770	0.2634	0.8851	12
0.0272	0.2209	0.0656	0.9691	0.1372	0.4141	0.2886	0.8899	13
0.0264	0.1981	0.0536	0.9557	0.1484	0.3703	0.2656	0.8862	14