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---
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: mms-1b-bigcgen-baseline-model
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. -->
# mms-1b-bigcgen-baseline-model
This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5130
- Wer: 0.4597
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use 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: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 12.3367 | 0.3058 | 100 | 1.3241 | 0.8893 |
| 1.8764 | 0.6116 | 200 | 0.6894 | 0.5815 |
| 1.6712 | 0.9174 | 300 | 0.6390 | 0.5514 |
| 1.5044 | 1.2232 | 400 | 0.6301 | 0.5351 |
| 1.6648 | 1.5291 | 500 | 0.6076 | 0.5283 |
| 1.6411 | 1.8349 | 600 | 0.6073 | 0.5283 |
| 1.4016 | 2.1407 | 700 | 0.5994 | 0.5124 |
| 1.5703 | 2.4465 | 800 | 0.5997 | 0.5162 |
| 1.4165 | 2.7523 | 900 | 0.5850 | 0.5084 |
| 1.4703 | 3.0581 | 1000 | 0.5912 | 0.5127 |
| 1.48 | 3.3639 | 1100 | 0.5707 | 0.4999 |
| 1.4769 | 3.6697 | 1200 | 0.5675 | 0.4949 |
| 1.312 | 3.9755 | 1300 | 0.5856 | 0.4980 |
| 1.3821 | 4.2813 | 1400 | 0.5642 | 0.4992 |
| 1.457 | 4.5872 | 1500 | 0.5588 | 0.5053 |
| 1.3606 | 4.8930 | 1600 | 0.5637 | 0.4866 |
| 1.3986 | 5.1988 | 1700 | 0.5511 | 0.4866 |
| 1.421 | 5.5046 | 1800 | 0.5846 | 0.5346 |
| 1.3004 | 5.8104 | 1900 | 0.5440 | 0.4736 |
| 1.3319 | 6.1162 | 2000 | 0.5318 | 0.4786 |
| 1.2665 | 6.4220 | 2100 | 0.5488 | 0.5065 |
| 1.3703 | 6.7278 | 2200 | 0.5304 | 0.4878 |
| 1.1954 | 7.0336 | 2300 | 0.5298 | 0.4807 |
| 1.2973 | 7.3394 | 2400 | 0.5258 | 0.4706 |
| 1.2086 | 7.6453 | 2500 | 0.5231 | 0.4807 |
| 1.2796 | 7.9511 | 2600 | 0.5404 | 0.4739 |
| 1.1428 | 8.2569 | 2700 | 0.5328 | 0.4831 |
| 1.3118 | 8.5627 | 2800 | 0.5198 | 0.4769 |
| 1.2569 | 8.8685 | 2900 | 0.5306 | 0.4847 |
| 1.1718 | 9.1743 | 3000 | 0.5160 | 0.4649 |
| 1.1354 | 9.4801 | 3100 | 0.5265 | 0.4777 |
| 1.2795 | 9.7859 | 3200 | 0.5090 | 0.4590 |
| 1.1793 | 10.0917 | 3300 | 0.5265 | 0.4684 |
| 1.1647 | 10.3976 | 3400 | 0.5385 | 0.4762 |
| 1.1978 | 10.7034 | 3500 | 0.5132 | 0.4715 |
| 1.1802 | 11.0092 | 3600 | 0.5130 | 0.4597 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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