Regression_BERT_NOaug_MSEloss

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

• Loss: 0.4928
• Mse: 0.4928
• Mae: 0.6337
• R2: 0.0926
• Accuracy: 0.4737

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:

• learning_rate: 2e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Mse	Mae	R2	Accuracy
No log	1.0	33	0.3184	0.3184	0.5205	0.0487	0.5946
No log	2.0	66	0.2439	0.2439	0.3571	0.2712	0.7027
No log	3.0	99	0.2950	0.2950	0.3792	0.1185	0.6757
No log	4.0	132	0.3179	0.3179	0.4267	0.0503	0.6757
No log	5.0	165	0.2869	0.2869	0.3984	0.1426	0.6757
No log	6.0	198	0.2967	0.2967	0.3688	0.1134	0.7027
No log	7.0	231	0.2797	0.2797	0.3599	0.1643	0.7027
No log	8.0	264	0.2730	0.2730	0.3438	0.1844	0.7027
No log	9.0	297	0.2813	0.2813	0.3623	0.1596	0.7027
No log	10.0	330	0.2733	0.2733	0.3296	0.1835	0.7027
No log	11.0	363	0.2770	0.2770	0.3432	0.1725	0.7027
No log	12.0	396	0.3009	0.3009	0.3574	0.1010	0.6757
No log	13.0	429	0.2735	0.2735	0.3318	0.1827	0.7027
No log	14.0	462	0.2787	0.2787	0.3341	0.1672	0.7027
No log	15.0	495	0.2790	0.2790	0.3312	0.1663	0.7297
0.0804	16.0	528	0.2683	0.2683	0.3229	0.1984	0.7027
0.0804	17.0	561	0.2749	0.2749	0.3273	0.1785	0.7297
0.0804	18.0	594	0.2709	0.2709	0.3202	0.1906	0.7297
0.0804	19.0	627	0.2711	0.2711	0.3205	0.1901	0.7297
0.0804	20.0	660	0.2694	0.2694	0.3197	0.1950	0.7297

Framework versions

• Transformers 4.28.1
• Pytorch 2.0.0+cu118
• Datasets 2.12.0
• Tokenizers 0.13.3