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

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/krishnan-aravind/huggingface/runs/e0c2wxc6)
# malayalam_combined_

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: 0.5153
- Wer: 0.5077

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- 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: 50
- num_epochs: 25
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 0.8243        | 0.2031 | 500   | 0.8413          | 0.6658 |
| 0.7336        | 0.4063 | 1000  | 0.7351          | 0.6251 |
| 0.6824        | 0.6094 | 1500  | 0.6786          | 0.5956 |
| 0.6489        | 0.8125 | 2000  | 0.6836          | 0.6075 |
| 0.585         | 1.0156 | 2500  | 0.6295          | 0.5864 |
| 0.5917        | 1.2188 | 3000  | 0.6166          | 0.5579 |
| 0.56          | 1.4219 | 3500  | 0.6006          | 0.5646 |
| 0.5736        | 1.6250 | 4000  | 0.6268          | 0.5643 |
| 0.5821        | 1.8282 | 4500  | 0.6216          | 0.5786 |
| 0.5505        | 2.0313 | 5000  | 0.5705          | 0.5379 |
| 0.5065        | 2.2344 | 5500  | 0.5864          | 0.5460 |
| 0.5004        | 2.4375 | 6000  | 0.5555          | 0.5259 |
| 0.5327        | 2.6407 | 6500  | 0.5539          | 0.5255 |
| 0.5148        | 2.8438 | 7000  | 0.5584          | 0.5457 |
| 0.4751        | 3.0469 | 7500  | 0.5389          | 0.5208 |
| 0.4779        | 3.2501 | 8000  | 0.5284          | 0.5102 |
| 0.4874        | 3.4532 | 8500  | 0.5300          | 0.5084 |
| 0.4955        | 3.6563 | 9000  | 0.5248          | 0.5125 |
| 0.4961        | 3.8594 | 9500  | 0.5116          | 0.5061 |
| 0.4449        | 4.0626 | 10000 | 0.5257          | 0.5122 |
| 0.48          | 4.2657 | 10500 | 0.5254          | 0.5046 |
| 0.4513        | 4.4688 | 11000 | 0.5364          | 0.5232 |
| 0.4698        | 4.6719 | 11500 | 0.5293          | 0.5106 |
| 0.4674        | 4.8751 | 12000 | 0.5153          | 0.5077 |


### Framework versions

- Transformers 4.43.0.dev0
- Pytorch 1.14.0a0+44dac51
- Datasets 2.16.1
- Tokenizers 0.19.1