---
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: ssc-bew-mms-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. -->

# ssc-bew-mms-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: 0.7747
- Cer: 0.2038
- Wer: 0.6111

## 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: 8
- eval_batch_size: 12
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Cer    | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| 1.6619        | 0.3063 | 200  | 0.9694          | 0.2219 | 0.6669 |
| 1.5435        | 0.6126 | 400  | 0.8620          | 0.2158 | 0.6542 |
| 1.5126        | 0.9188 | 600  | 0.8212          | 0.2101 | 0.6326 |
| 1.4544        | 1.2251 | 800  | 0.8192          | 0.2101 | 0.6346 |
| 1.4753        | 1.5314 | 1000 | 0.8128          | 0.2096 | 0.6393 |
| 1.4388        | 1.8377 | 1200 | 0.8112          | 0.2098 | 0.6292 |
| 1.4494        | 2.1440 | 1400 | 0.8120          | 0.2084 | 0.6268 |
| 1.4289        | 2.4502 | 1600 | 0.7985          | 0.2075 | 0.6254 |
| 1.4736        | 2.7565 | 1800 | 0.7951          | 0.2070 | 0.6263 |
| 1.4217        | 3.0628 | 2000 | 0.8154          | 0.2104 | 0.6369 |
| 1.4146        | 3.3691 | 2200 | 0.8092          | 0.2094 | 0.6295 |
| 1.4205        | 3.6753 | 2400 | 0.8056          | 0.2092 | 0.6298 |
| 1.3951        | 3.9816 | 2600 | 0.8070          | 0.2087 | 0.6384 |
| 1.3695        | 4.2879 | 2800 | 0.7980          | 0.2078 | 0.6261 |
| 1.4743        | 4.5942 | 3000 | 0.8138          | 0.2099 | 0.6376 |
| 1.4499        | 4.9005 | 3200 | 0.7828          | 0.2041 | 0.6168 |
| 1.3801        | 5.2067 | 3400 | 0.8019          | 0.2086 | 0.6324 |
| 1.4191        | 5.5130 | 3600 | 0.7931          | 0.2064 | 0.6229 |
| 1.4359        | 5.8193 | 3800 | 0.7794          | 0.2034 | 0.6144 |
| 1.407         | 6.1256 | 4000 | 0.7877          | 0.2064 | 0.6243 |
| 1.4079        | 6.4319 | 4200 | 0.7963          | 0.2071 | 0.6221 |
| 1.417         | 6.7381 | 4400 | 0.7839          | 0.2048 | 0.6139 |
| 1.4031        | 7.0444 | 4600 | 0.7759          | 0.2033 | 0.6098 |
| 1.3999        | 7.3507 | 4800 | 0.7781          | 0.2043 | 0.6137 |
| 1.4052        | 7.6570 | 5000 | 0.7769          | 0.2041 | 0.6110 |
| 1.4102        | 7.9632 | 5200 | 0.7750          | 0.2031 | 0.6085 |
| 1.392         | 8.2695 | 5400 | 0.7776          | 0.2041 | 0.6115 |
| 1.4032        | 8.5758 | 5600 | 0.7733          | 0.2033 | 0.6096 |
| 1.3956        | 8.8821 | 5800 | 0.7761          | 0.2038 | 0.6101 |
| 1.4148        | 9.1884 | 6000 | 0.7761          | 0.2040 | 0.6102 |
| 1.3921        | 9.4946 | 6200 | 0.7746          | 0.2036 | 0.6099 |
| 1.4109        | 9.8009 | 6400 | 0.7747          | 0.2038 | 0.6111 |


### Framework versions

- Transformers 4.57.2
- Pytorch 2.9.1+cu128
- Datasets 3.6.0
- Tokenizers 0.22.0