---
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