Regression_xlnet_NOaug_CustomLoss

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

• Train Loss: 0.1862
• Train Mae: 0.5631
• Train Mse: 0.4095
• Train R2-score: 0.8268
• Validation Loss: 0.1355
• Validation Mae: 0.5683
• Validation Mse: 0.3643
• Validation R2-score: 0.8811
• Epoch: 14

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': 1e-04, '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	Validation Loss	Validation Mae	Validation Mse	Validation R2-score	Epoch
0.1966	0.5177	0.3647	0.3590	0.1412	0.6460	0.4895	0.8850	0
0.1804	0.5606	0.4181	0.8105	0.1540	0.6614	0.5259	0.8820	1
0.2037	0.5676	0.4319	0.6885	0.1399	0.6439	0.4849	0.8849	2
0.1833	0.5499	0.3954	0.8256	0.1804	0.6845	0.5879	0.8760	3
0.1627	0.5412	0.3866	0.8022	0.1661	0.6729	0.5558	0.8793	4
0.1822	0.5677	0.4178	0.7449	0.1327	0.6311	0.4580	0.8861	5
0.2117	0.5798	0.4520	0.5186	0.1282	0.6187	0.4345	0.8866	6
0.1843	0.5544	0.3998	0.5283	0.1272	0.6142	0.4265	0.8866	7
0.2074	0.5906	0.4639	0.6729	0.1269	0.6127	0.4239	0.8865	8
0.1756	0.5666	0.4032	0.8054	0.1272	0.5909	0.3908	0.8850	9
0.1706	0.5452	0.3948	0.7999	0.1282	0.5862	0.3845	0.8844	10
0.1727	0.5499	0.3928	0.8471	0.1453	0.6513	0.5021	0.8840	11
0.1688	0.5467	0.3884	0.3339	0.1777	0.6823	0.5817	0.8766	12
0.1625	0.5476	0.3918	0.5804	0.1483	0.6541	0.5098	0.8833	13
0.1862	0.5631	0.4095	0.8268	0.1355	0.5683	0.3643	0.8811	14

Framework versions

• Transformers 4.28.1
• TensorFlow 2.12.0
• Datasets 2.12.0
• Tokenizers 0.13.3