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---
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
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