| --- |
| library_name: transformers |
| tags: |
| - generated_from_trainer |
| metrics: |
| - wer |
| model-index: |
| - name: MSP-Visual |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # MSP-Visual |
|
|
| This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.3334 |
| - Wer: 0.6493 |
| - Cer: 0.3725 |
|
|
| ## 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: 0.0001 |
| - train_batch_size: 32 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_steps: 1000.0 |
| - training_steps: 20000 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |
| |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| |
| | 2.5771 | 0.05 | 1000 | 2.3988 | 0.9791 | 0.6114 | |
| | 2.0987 | 0.1 | 2000 | 1.7770 | 0.8288 | 0.4786 | |
| | 1.9764 | 0.15 | 3000 | 1.6459 | 0.7827 | 0.4485 | |
| | 1.9493 | 0.2 | 4000 | 1.6139 | 0.7614 | 0.4388 | |
| | 1.9069 | 0.25 | 5000 | 1.5498 | 0.7351 | 0.4191 | |
| | 1.9144 | 0.3 | 6000 | 1.5212 | 0.7212 | 0.4142 | |
| | 1.8289 | 0.35 | 7000 | 1.4857 | 0.7139 | 0.4063 | |
| | 1.8845 | 0.4 | 8000 | 1.4832 | 0.6958 | 0.3979 | |
| | 1.7763 | 0.45 | 9000 | 1.4466 | 0.6938 | 0.3946 | |
| | 1.9370 | 0.5 | 10000 | 1.4235 | 0.6825 | 0.3916 | |
| | 1.7678 | 0.55 | 11000 | 1.4164 | 0.6784 | 0.3857 | |
| | 1.8403 | 0.6 | 12000 | 1.3981 | 0.6696 | 0.3868 | |
| | 1.6723 | 0.65 | 13000 | 1.3849 | 0.6631 | 0.3769 | |
| | 1.7040 | 0.7 | 14000 | 1.3884 | 0.6579 | 0.3763 | |
| | 1.5821 | 0.75 | 15000 | 1.3599 | 0.6588 | 0.3756 | |
| | 1.5721 | 0.8 | 16000 | 1.3480 | 0.6519 | 0.3728 | |
| | 1.6635 | 0.85 | 17000 | 1.3432 | 0.6527 | 0.3732 | |
| | 1.6557 | 0.9 | 18000 | 1.3501 | 0.6525 | 0.3748 | |
| | 1.7992 | 0.95 | 19000 | 1.3334 | 0.6493 | 0.3725 | |
| | 1.7090 | 1.0 | 20000 | 1.3328 | 0.6499 | 0.3724 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 5.10.2 |
| - Pytorch 2.8.0+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.2 |
|
|
