---
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: mms-MGB2
  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-MGB2

This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Wer: 1.0

## 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: 1e-05
- train_batch_size: 14
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 10000

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 6.8035        | 0.01  | 250   | 5.9077          | 1.0036 |
| 2.2196        | 0.02  | 500   | 2.0224          | 0.9764 |
| 1.0641        | 0.03  | 750   | 0.8949          | 0.4840 |
| 0.8089        | 0.04  | 1000  | 0.7188          | 0.4095 |
| 1.7071        | 0.05  | 1250  | 0.7008          | 0.3974 |
| 0.8132        | 0.06  | 1500  | 0.6975          | 0.3986 |
| 0.9741        | 0.07  | 1750  | 0.6975          | 0.3986 |
| 0.8332        | 0.08  | 2000  | 0.6975          | 0.3986 |
| 0.8908        | 0.09  | 2250  | 0.6975          | 0.3986 |
| 0.8321        | 0.1   | 2500  | 0.6975          | 0.3986 |
| 0.7957        | 0.1   | 2750  | 0.6975          | 0.3986 |
| 0.9173        | 0.11  | 3000  | 0.6975          | 0.3986 |
| 2.0065        | 0.12  | 3250  | 0.6975          | 0.3986 |
| 0.8618        | 0.13  | 3500  | 0.6975          | 0.3986 |
| 0.9001        | 0.14  | 3750  | 0.6975          | 0.3986 |
| 1.0321        | 0.15  | 4000  | 0.6975          | 0.3986 |
| 0.8408        | 0.16  | 4250  | 0.6975          | 0.3986 |
| 0.8901        | 0.17  | 4500  | 0.6975          | 0.3986 |
| 0.8242        | 0.18  | 4750  | 0.6975          | 0.3986 |
| 0.8678        | 0.19  | 5000  | 0.6975          | 0.3986 |
| 0.8633        | 0.2   | 5250  | 0.6975          | 0.3986 |
| 0.8087        | 0.21  | 5500  | 0.6975          | 0.3986 |
| 0.9243        | 0.22  | 5750  | 0.6975          | 0.3986 |
| 0.7973        | 0.23  | 6000  | 0.6975          | 0.3986 |
| 0.835         | 0.24  | 6250  | 0.6975          | 0.3986 |
| 1.3251        | 0.25  | 6500  | 0.6975          | 0.3986 |
| 0.0           | 0.26  | 6750  | nan             | 1.0    |
| 0.0           | 0.27  | 7000  | nan             | 1.0    |
| 0.0           | 0.28  | 7250  | nan             | 1.0    |
| 0.0           | 0.29  | 7500  | nan             | 1.0    |
| 0.0           | 0.29  | 7750  | nan             | 1.0    |
| 0.0           | 0.3   | 8000  | nan             | 1.0    |
| 0.0           | 0.31  | 8250  | nan             | 1.0    |
| 0.0           | 0.32  | 8500  | nan             | 1.0    |
| 0.0           | 0.33  | 8750  | nan             | 1.0    |
| 0.0           | 0.34  | 9000  | nan             | 1.0    |
| 0.0           | 0.35  | 9250  | nan             | 1.0    |
| 0.0           | 0.36  | 9500  | nan             | 1.0    |
| 0.0           | 0.37  | 9750  | nan             | 1.0    |
| 0.0           | 0.38  | 10000 | nan             | 1.0    |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1
- Datasets 2.19.1
- Tokenizers 0.13.3