name-parser-small

This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0124
• Accuracy: 0.9947

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: 4
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• optimizer: Use adafactor and the args are: No additional optimizer arguments
• lr_scheduler_type: linear
• training_steps: 20000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.032	0.7150	2000	0.0202	0.9922
0.0234	1.4297	4000	0.0164	0.9936
0.0203	2.1444	6000	0.0146	0.9941
0.0187	2.8594	8000	0.0138	0.9942
0.0178	3.5741	10000	0.0133	0.9943
0.0173	4.2889	12000	0.0130	0.9944
0.0175	5.0036	14000	0.0126	0.9946
0.0164	5.7186	16000	0.0124	0.9947
0.0165	6.4333	18000	0.0124	0.9947
0.0166	7.1480	20000	0.0124	0.9947

Framework versions

• Transformers 4.52.3
• Pytorch 2.7.0+cu126
• Datasets 3.6.0
• Tokenizers 0.21.1