---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- fleurs
metrics:
- wer
model-index:
- name: w2v-bert2-pashto-augmented
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: fleurs
      type: fleurs
      config: ps_af
      split: test
      args: ps_af
    metrics:
    - name: Wer
      type: wer
      value: 0.34313876482365624
---

<!-- 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-bert2-pashto-augmented

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5954
- Wer: 0.3431

## 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: 700
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 3.0422        | 1.1713 | 100  | 3.0380          | 0.9640 |
| 2.3141        | 2.3426 | 200  | 2.0336          | 0.9464 |
| 0.7365        | 3.5139 | 300  | 0.6768          | 0.4520 |
| 0.557         | 4.6852 | 400  | 0.6051          | 0.3913 |
| 0.5101        | 5.8565 | 500  | 0.6571          | 0.3853 |
| 0.3803        | 7.0278 | 600  | 0.5946          | 0.3497 |
| 0.2452        | 8.1991 | 700  | 0.5954          | 0.3431 |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1