| --- |
| library_name: transformers |
| tags: |
| - generated_from_trainer |
| metrics: |
| - wer |
| model-index: |
| - name: MSP-Fusion |
| 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-Fusion |
|
|
| 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: 0.4092 |
| - Wer: 0.2350 |
| - Cer: 0.1167 |
|
|
| ## 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: 0.0002 |
| - 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: 500 |
| - training_steps: 10000 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |
| |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| |
| | 0.9948 | 0.05 | 500 | 0.4387 | 0.2571 | 0.1277 | |
| | 1.1610 | 0.1 | 1000 | 0.4477 | 0.2550 | 0.1237 | |
| | 0.9327 | 0.15 | 1500 | 0.4221 | 0.2414 | 0.1188 | |
| | 1.0052 | 0.2 | 2000 | 0.4231 | 0.2476 | 0.1231 | |
| | 0.9512 | 0.25 | 2500 | 0.4339 | 0.2556 | 0.1288 | |
| | 0.9423 | 0.3 | 3000 | 0.4389 | 0.2580 | 0.1278 | |
| | 0.8131 | 0.35 | 3500 | 0.4282 | 0.2489 | 0.1247 | |
| | 0.9237 | 0.4 | 4000 | 0.4336 | 0.2464 | 0.1219 | |
| | 0.9842 | 0.45 | 4500 | 0.4305 | 0.2559 | 0.1290 | |
| | 1.0400 | 0.5 | 5000 | 0.4241 | 0.2426 | 0.1199 | |
| | 0.9045 | 0.55 | 5500 | 0.4265 | 0.2449 | 0.1215 | |
| | 0.9655 | 0.6 | 6000 | 0.4266 | 0.2430 | 0.1204 | |
| | 0.8683 | 0.65 | 6500 | 0.4139 | 0.2379 | 0.1178 | |
| | 0.8457 | 0.7 | 7000 | 0.4124 | 0.2372 | 0.1178 | |
| | 0.9675 | 0.75 | 7500 | 0.4133 | 0.2369 | 0.1189 | |
| | 0.9740 | 0.8 | 8000 | 0.4178 | 0.2406 | 0.1208 | |
| | 0.9536 | 0.85 | 8500 | 0.4092 | 0.2350 | 0.1167 | |
| | 0.9858 | 0.9 | 9000 | 0.4144 | 0.2378 | 0.1184 | |
| | 0.9754 | 0.95 | 9500 | 0.4113 | 0.2366 | 0.1174 | |
| | 0.9603 | 1.0 | 10000 | 0.4126 | 0.2373 | 0.1177 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 5.10.2 |
| - Pytorch 2.8.0+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.2 |
|
|
