Bert_uncased_fine_tuned_Reward_Model

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

• Loss: 0.0876
• Mse: 0.0876
• Mae: 0.1403
• R2: 0.7389
• Accuracy: 0.875

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: 16
• eval_batch_size: 16
• 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	53	0.1744	0.1744	0.2973	0.4805	0.7885
No log	2.0	106	0.1074	0.1074	0.2333	0.6801	0.8846
No log	3.0	159	0.1026	0.1026	0.2134	0.6943	0.8654
No log	4.0	212	0.0877	0.0877	0.1841	0.7388	0.8942
No log	5.0	265	0.1000	0.1000	0.2007	0.7021	0.8942
No log	6.0	318	0.0863	0.0863	0.1738	0.7429	0.8942
No log	7.0	371	0.0966	0.0966	0.1827	0.7122	0.8846
No log	8.0	424	0.0946	0.0946	0.1701	0.7183	0.8846
No log	9.0	477	0.0978	0.0978	0.1658	0.7088	0.875
0.0516	10.0	530	0.0854	0.0854	0.1639	0.7457	0.875
0.0516	11.0	583	0.0947	0.0947	0.1620	0.7181	0.8846
0.0516	12.0	636	0.0907	0.0907	0.1516	0.7297	0.8846
0.0516	13.0	689	0.0885	0.0885	0.1546	0.7364	0.875
0.0516	14.0	742	0.0849	0.0849	0.1452	0.7471	0.8942
0.0516	15.0	795	0.0823	0.0823	0.1428	0.7548	0.8846
0.0516	16.0	848	0.0864	0.0864	0.1429	0.7427	0.8846
0.0516	17.0	901	0.0854	0.0854	0.1427	0.7457	0.8846
0.0516	18.0	954	0.0860	0.0860	0.1429	0.7437	0.875
0.0059	19.0	1007	0.0871	0.0871	0.1438	0.7406	0.875
0.0059	20.0	1060	0.0876	0.0876	0.1403	0.7389	0.875

Framework versions

• Transformers 4.26.1
• Pytorch 1.13.1+cu116
• Datasets 2.10.1
• Tokenizers 0.13.2