rest15_50

This model is a fine-tuned version of google-t5/t5-small on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.0418
• Precision: 0.2766
• Recall: 0.1873
• F1: 0.2234

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: 0.0003
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 20
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1
No log	1.0	7	3.6671	0	0.0	0
No log	2.0	14	2.2951	0.0	0.0	0
No log	3.0	21	1.7160	0.1111	0.0202	0.0341
No log	4.0	28	1.4462	0.1301	0.0548	0.0771
No log	5.0	35	1.2381	0.1789	0.098	0.1266
No log	6.0	42	1.1546	0.2047	0.1268	0.1566
No log	7.0	49	1.1470	0.2242	0.1441	0.1754
No log	8.0	56	1.1186	0.2385	0.1499	0.1841
No log	9.0	63	1.1021	0.2511	0.17	0.2027
No log	10.0	70	1.0770	0.2533	0.1671	0.2014
No log	11.0	77	1.0839	0.2596	0.1556	0.1946
No log	12.0	84	1.0612	0.2667	0.1729	0.2098
No log	13.0	91	1.0618	0.2739	0.1816	0.2184
No log	14.0	98	1.0613	0.2641	0.1758	0.2111
No log	15.0	105	1.0557	0.2727	0.1816	0.218
No log	16.0	112	1.0416	0.2573	0.1787	0.2109
No log	17.0	119	1.0418	0.2746	0.1931	0.2267
No log	18.0	126	1.0438	0.2797	0.1902	0.2264
No log	19.0	133	1.0432	0.2754	0.1873	0.223
No log	20.0	140	1.0418	0.2766	0.1873	0.2234

Framework versions

• Transformers 4.39.1
• Pytorch 2.7.0+cu118
• Datasets 2.12.0
• Tokenizers 0.15.2