ssc-ruc-mms-model-mix-adapt-max3

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.6142
• Cer: 0.1543
• Wer: 0.6390

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: 0.0005
• train_batch_size: 1
• eval_batch_size: 6
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 2
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 100
• num_epochs: 5
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Cer	Wer
0.5848	0.2972	200	0.6128	0.1617	0.6682
0.4815	0.5944	400	0.6463	0.1638	0.6670
0.4763	0.8915	600	0.6403	0.1596	0.6550
0.5707	1.1887	800	0.6172	0.1573	0.6545
0.4471	1.4859	1000	0.6273	0.1556	0.6477
0.4728	1.7831	1200	0.6206	0.1574	0.6591
0.4538	2.0802	1400	0.6268	0.1569	0.6481
0.4975	2.3774	1600	0.6319	0.1565	0.6565
0.4175	2.6746	1800	0.6283	0.1558	0.6452
0.4897	2.9718	2000	0.6153	0.1568	0.6449
0.3867	3.2689	2200	0.6198	0.1546	0.6415
0.5008	3.5661	2400	0.6156	0.1546	0.6404
0.6046	3.8633	2600	0.6144	0.1539	0.6362
0.4339	4.1605	2800	0.6142	0.1535	0.6404
0.4779	4.4577	3000	0.6148	0.1544	0.6395
0.4157	4.7548	3200	0.6142	0.1543	0.6390

Framework versions

• Transformers 4.52.1
• Pytorch 2.9.1+cu128
• Datasets 3.6.0
• Tokenizers 0.21.4