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---
library_name: transformers
language:
- lg
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- Grain
metrics:
- wer
model-index:
- name: w
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Grain
type: Grain
metrics:
- name: Wer
type: wer
value: 0.029878515924263983
---
<!-- 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. -->
# w
This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co./facebook/w2v-bert-2.0) on the Grain dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0469
- Wer: 0.0299
- Cer: 0.0077
## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 0.2995 | 1.0 | 1164 | 0.1521 | 0.1390 | 0.0283 |
| 0.1049 | 2.0 | 2328 | 0.0931 | 0.0946 | 0.0189 |
| 0.0719 | 3.0 | 3492 | 0.0861 | 0.0902 | 0.0183 |
| 0.0546 | 4.0 | 4656 | 0.0788 | 0.0704 | 0.0166 |
| 0.0447 | 5.0 | 5820 | 0.0609 | 0.0627 | 0.0135 |
| 0.0374 | 6.0 | 6984 | 0.0744 | 0.0618 | 0.0141 |
| 0.0338 | 7.0 | 8148 | 0.0673 | 0.0535 | 0.0137 |
| 0.029 | 8.0 | 9312 | 0.0770 | 0.0540 | 0.0128 |
| 0.0278 | 9.0 | 10476 | 0.0565 | 0.0482 | 0.0116 |
| 0.0227 | 10.0 | 11640 | 0.0516 | 0.0500 | 0.0115 |
| 0.0211 | 11.0 | 12804 | 0.0457 | 0.0392 | 0.0096 |
| 0.0207 | 12.0 | 13968 | 0.0527 | 0.0452 | 0.0098 |
| 0.0179 | 13.0 | 15132 | 0.0463 | 0.0370 | 0.0089 |
| 0.017 | 14.0 | 16296 | 0.0530 | 0.0452 | 0.0109 |
| 0.0167 | 15.0 | 17460 | 0.0447 | 0.0360 | 0.0091 |
| 0.0141 | 16.0 | 18624 | 0.0529 | 0.0434 | 0.0104 |
| 0.015 | 17.0 | 19788 | 0.0410 | 0.0387 | 0.0090 |
| 0.0141 | 18.0 | 20952 | 0.0480 | 0.0416 | 0.0102 |
| 0.0136 | 19.0 | 22116 | 0.0472 | 0.0368 | 0.0087 |
| 0.0125 | 20.0 | 23280 | 0.0428 | 0.0380 | 0.0091 |
| 0.0117 | 21.0 | 24444 | 0.0375 | 0.0328 | 0.0081 |
| 0.0113 | 22.0 | 25608 | 0.0392 | 0.0312 | 0.0083 |
| 0.0093 | 23.0 | 26772 | 0.0554 | 0.0394 | 0.0102 |
| 0.0111 | 24.0 | 27936 | 0.0624 | 0.0452 | 0.0108 |
| 0.0107 | 25.0 | 29100 | 0.0390 | 0.0346 | 0.0076 |
| 0.0082 | 26.0 | 30264 | 0.0505 | 0.0426 | 0.0101 |
| 0.0087 | 27.0 | 31428 | 0.0430 | 0.0320 | 0.0081 |
| 0.0086 | 28.0 | 32592 | 0.0541 | 0.0398 | 0.0101 |
| 0.0079 | 29.0 | 33756 | 0.0404 | 0.0304 | 0.0070 |
| 0.0084 | 30.0 | 34920 | 0.0416 | 0.0315 | 0.0075 |
| 0.0084 | 31.0 | 36084 | 0.0495 | 0.0366 | 0.0092 |
| 0.0075 | 32.0 | 37248 | 0.0469 | 0.0299 | 0.0077 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.1.0+cu118
- Datasets 3.0.1
- Tokenizers 0.20.1
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