---
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Finetuned-mT5-Bangla2IPA
  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. -->

# Finetuned-mT5-Bangla2IPA

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0529
- Wer: 0.0530

## 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: 0.0003
- train_batch_size: 4
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.407         | 0.75  | 2000  | 1.4085          | 0.9726 |
| 0.9875        | 1.5   | 4000  | 0.2242          | 0.2198 |
| 0.3856        | 2.25  | 6000  | 0.1268          | 0.1266 |
| 0.2443        | 2.99  | 8000  | 0.0929          | 0.0958 |
| 0.1753        | 3.74  | 10000 | 0.0798          | 0.0827 |
| 0.1431        | 4.49  | 12000 | 0.0701          | 0.0731 |
| 0.121         | 5.24  | 14000 | 0.0649          | 0.0665 |
| 0.1046        | 5.99  | 16000 | 0.0614          | 0.0625 |
| 0.0914        | 6.74  | 18000 | 0.0585          | 0.0588 |
| 0.0821        | 7.49  | 20000 | 0.0556          | 0.0564 |
| 0.0747        | 8.23  | 22000 | 0.0554          | 0.0548 |
| 0.0707        | 8.98  | 24000 | 0.0535          | 0.0540 |
| 0.0646        | 9.73  | 26000 | 0.0529          | 0.0530 |


### Framework versions

- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3