Regression_roberta_1

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

• Train Loss: 0.3891
• Train Mae: 0.3117
• Train Mse: 0.1477
• Train R2-score: 0.7113
• Train Accuracy: 0.7077
• Validation Loss: 0.3272
• Validation Mae: 0.3256
• Validation Mse: 0.1253
• Validation R2-score: 0.8839
• Validation Accuracy: 0.9459
• Epoch: 9

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

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': 2e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Train Mae	Train Mse	Train R2-score	Train Accuracy	Validation Loss	Validation Mae	Validation Mse	Validation R2-score	Validation Accuracy	Epoch
0.4486	0.2939	0.1319	0.7250	0.7769	0.4177	0.4165	0.2221	0.8321	0.3243	0
0.3684	0.2898	0.1342	0.5541	0.7462	0.4019	0.4006	0.2091	0.8409	0.3243	1
0.3423	0.2854	0.1299	0.7355	0.7462	0.3971	0.3958	0.2050	0.8438	0.3243	2
0.3514	0.2890	0.1324	0.7935	0.7538	0.3552	0.3538	0.1640	0.8681	0.9459	3
0.3722	0.3107	0.1525	0.5604	0.7000	0.3448	0.3432	0.1484	0.8750	0.9459	4
0.3996	0.2949	0.1305	0.7869	0.8231	0.3692	0.3677	0.1794	0.8514	0.4865	5
0.3441	0.2895	0.1322	0.7546	0.7538	0.3186	0.3169	0.1159	0.8860	0.9459	6
0.3898	0.2921	0.1255	0.5919	0.7692	0.4107	0.4095	0.2160	0.8366	0.3243	7
0.3552	0.2868	0.1297	0.7113	0.7538	0.4426	0.4415	0.2434	0.8179	0.3243	8
0.3891	0.3117	0.1477	0.7113	0.7077	0.3272	0.3256	0.1253	0.8839	0.9459	9

Framework versions

• Transformers 4.27.2
• TensorFlow 2.11.0
• Datasets 2.10.1
• Tokenizers 0.13.2