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---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v-bert-2.0-odia_v1
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. -->
# w2v-bert-2.0-odia_v1
This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0767
- Wer: 0.1256
## 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: 3.5356e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.4216 | 0.3733 | 300 | 0.2149 | 0.3309 |
| 0.2996 | 0.7465 | 600 | 0.1719 | 0.2572 |
| 0.2271 | 1.1198 | 900 | 0.1366 | 0.2390 |
| 0.1917 | 1.4930 | 1200 | 0.1137 | 0.2054 |
| 0.167 | 1.8663 | 1500 | 0.1208 | 0.2046 |
| 0.1371 | 2.2395 | 1800 | 0.0995 | 0.1995 |
| 0.133 | 2.6128 | 2100 | 0.1006 | 0.1944 |
| 0.1214 | 2.9860 | 2400 | 0.0958 | 0.1715 |
| 0.101 | 3.3593 | 2700 | 0.0853 | 0.1602 |
| 0.1007 | 3.7325 | 3000 | 0.0851 | 0.1667 |
| 0.0898 | 4.1058 | 3300 | 0.0820 | 0.1532 |
| 0.089 | 4.4790 | 3600 | 0.0814 | 0.1539 |
| 0.0776 | 4.8523 | 3900 | 0.0792 | 0.1479 |
| 0.0655 | 5.2255 | 4200 | 0.0782 | 0.1438 |
| 0.0708 | 5.5988 | 4500 | 0.0770 | 0.1391 |
| 0.0662 | 5.9720 | 4800 | 0.0727 | 0.1372 |
| 0.0556 | 6.3453 | 5100 | 0.0757 | 0.1372 |
| 0.0629 | 6.7185 | 5400 | 0.0729 | 0.1319 |
| 0.0472 | 7.0918 | 5700 | 0.0771 | 0.1369 |
| 0.0546 | 7.4650 | 6000 | 0.0760 | 0.1378 |
| 0.041 | 7.8383 | 6300 | 0.0750 | 0.1402 |
| 0.0405 | 8.2115 | 6600 | 0.0776 | 0.1340 |
| 0.0395 | 8.5848 | 6900 | 0.0741 | 0.1306 |
| 0.0366 | 8.9580 | 7200 | 0.0742 | 0.1255 |
| 0.0288 | 9.3313 | 7500 | 0.0767 | 0.1296 |
| 0.0329 | 9.7045 | 7800 | 0.0767 | 0.1256 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.1.2+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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