Regression_bert_aug_CustomLoss_2

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
0.2004	0.4982	0.3687	0.6907	0.1488	0.4239	0.3023	0.7428	0
0.1118	0.4054	0.2460	0.7552	0.0783	0.3502	0.1873	0.8501	1
0.0531	0.3256	0.1543	0.8049	0.0489	0.3257	0.1489	0.8412	2
0.0342	0.2826	0.1151	0.7986	0.0328	0.2697	0.1215	0.9246	3
0.0266	0.2587	0.0962	0.8802	0.0713	0.2884	0.1297	0.8729	4
0.0543	0.3022	0.1388	0.7724	0.0609	0.3238	0.1524	0.7723	5
0.0380	0.2756	0.1114	0.8822	0.0421	0.1984	0.0700	0.9070	6
0.0593	0.3134	0.1537	0.8764	0.2335	0.5183	0.4636	0.7816	7
0.2330	0.5234	0.4182	-1.5020	0.2656	0.5726	0.3948	0.5533	8
0.2359	0.5149	0.4195	0.7932	0.2347	0.5263	0.4461	0.7502	9
0.2341	0.5204	0.4268	0.8100	0.2341	0.5509	0.4307	0.7220	10
0.2335	0.5250	0.4235	0.8053	0.2328	0.5433	0.4334	0.7322	11
0.2319	0.5217	0.4244	0.7825	0.2352	0.5549	0.4296	0.7158	12
0.2323	0.5243	0.4234	0.7877	0.2346	0.5536	0.4286	0.7170	13
0.2338	0.5263	0.4258	0.7899	0.2340	0.5490	0.4329	0.7254	14