---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v-bert-Marathi-large
  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-Marathi-large

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: 0.2714
- Wer: 0.1698
- Cer: 0.0531

## 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: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| 2.8852        | 0.5882 | 300  | 0.7826          | 0.4911 | 0.1647 |
| 0.6243        | 1.1765 | 600  | 0.6280          | 0.3920 | 0.1351 |
| 0.4901        | 1.7647 | 900  | 0.4369          | 0.3101 | 0.0986 |
| 0.355         | 2.3529 | 1200 | 0.3922          | 0.2658 | 0.0849 |
| 0.2943        | 2.9412 | 1500 | 0.3400          | 0.2371 | 0.0753 |
| 0.2177        | 3.5294 | 1800 | 0.3041          | 0.2080 | 0.0646 |
| 0.1779        | 4.1176 | 2100 | 0.2906          | 0.1954 | 0.0608 |
| 0.1299        | 4.7059 | 2400 | 0.2904          | 0.1779 | 0.0560 |
| 0.0929        | 5.2941 | 2700 | 0.2885          | 0.1727 | 0.0537 |
| 0.0729        | 5.8824 | 3000 | 0.2714          | 0.1698 | 0.0531 |


### Framework versions

- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1