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---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: pashto-asr-v3
  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. -->

# pashto-asr-v3

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1448
- Wer: 0.1396

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1300
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 2.9698        | 0.8089  | 100  | 2.8928          | 0.9991 |
| 0.8095        | 1.6178  | 200  | 0.6035          | 0.4036 |
| 0.6152        | 2.4267  | 300  | 0.4857          | 0.3593 |
| 0.3951        | 3.2356  | 400  | 0.4661          | 0.3505 |
| 0.5493        | 4.0445  | 500  | 0.3651          | 0.2779 |
| 0.4588        | 4.8534  | 600  | 0.3244          | 0.2632 |
| 0.3616        | 5.6623  | 700  | 0.2954          | 0.2490 |
| 0.1938        | 6.4712  | 800  | 0.2655          | 0.2341 |
| 0.2047        | 7.2801  | 900  | 0.2510          | 0.2022 |
| 0.2596        | 8.0890  | 1000 | 0.1953          | 0.1756 |
| 0.1871        | 8.8979  | 1100 | 0.1716          | 0.1642 |
| 0.0768        | 9.7068  | 1200 | 0.1559          | 0.1554 |
| 0.1021        | 10.5157 | 1300 | 0.1448          | 0.1396 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1