---
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v-bert-punjabi_v2
  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-punjabi_v2

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: 0.2031
- Wer: 0.1135

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 60000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 0.4419        | 0.2174 | 2000  | 0.3828          | 0.2268 |
| 0.3492        | 0.4348 | 4000  | 0.3401          | 0.1836 |
| 0.3205        | 0.6522 | 6000  | 0.2932          | 0.1712 |
| 0.2813        | 0.8696 | 8000  | 0.2844          | 0.1590 |
| 0.255         | 1.0870 | 10000 | 0.2562          | 0.1469 |
| 0.2451        | 1.3043 | 12000 | 0.2431          | 0.1386 |
| 0.2305        | 1.5217 | 14000 | 0.2299          | 0.1312 |
| 0.2156        | 1.7391 | 16000 | 0.2191          | 0.1274 |
| 0.2119        | 1.9565 | 18000 | 0.2269          | 0.1205 |
| 0.182         | 2.1739 | 20000 | 0.2091          | 0.1181 |
| 0.1789        | 2.3913 | 22000 | 0.1980          | 0.1136 |
| 0.1766        | 2.6087 | 24000 | 0.1945          | 0.1092 |
| 0.1657        | 2.8261 | 26000 | 0.1881          | 0.1079 |
| 0.1461        | 3.0435 | 28000 | 0.1809          | 0.1050 |
| 0.1454        | 3.2609 | 30000 | 0.1810          | 0.1029 |
| 0.1697        | 3.4783 | 32000 | 0.2085          | 0.1210 |
| 0.1763        | 3.6957 | 34000 | 0.2017          | 0.1172 |
| 0.1642        | 3.9130 | 36000 | 0.2031          | 0.1135 |


### Framework versions

- Transformers 4.48.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0