transformers tensorboard safetensors whisper automatic-speech-recognition generated_from_trainer base_model:parambharat/whisper-small-ta base_model:finetune:parambharat/whisper-small-ta license:apache-2.0 endpoints_compatible region:us
---
base_model: parambharat/whisper-small-ta
library_name: transformers
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: MTF-ta-en-translation
  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. -->

# MTF-ta-en-translation

This model is a fine-tuned version of [parambharat/whisper-small-ta](https://huggingface.co/parambharat/whisper-small-ta) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1324
- Bleu Score: 0.0299

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Bleu Score |
|:-------------:|:-------:|:----:|:---------------:|:----------:|
| 0.0939        | 2.9412  | 250  | 0.0864          | 0.0302     |
| 0.0227        | 5.8824  | 500  | 0.0998          | 0.0301     |
| 0.0048        | 8.8235  | 750  | 0.1083          | 0.0340     |
| 0.001         | 11.7647 | 1000 | 0.1132          | 0.0312     |
| 0.0005        | 14.7059 | 1250 | 0.1164          | 0.0308     |
| 0.0003        | 17.6471 | 1500 | 0.1189          | 0.0322     |
| 0.0002        | 20.5882 | 1750 | 0.1208          | 0.0311     |
| 0.0002        | 23.5294 | 2000 | 0.1225          | 0.0307     |
| 0.0002        | 26.4706 | 2250 | 0.1242          | 0.0334     |
| 0.0001        | 29.4118 | 2500 | 0.1256          | 0.0321     |
| 0.0001        | 32.3529 | 2750 | 0.1268          | 0.0327     |
| 0.0001        | 35.2941 | 3000 | 0.1277          | 0.0324     |
| 0.0001        | 38.2353 | 3250 | 0.1286          | 0.0311     |
| 0.0001        | 41.1765 | 3500 | 0.1295          | 0.0309     |
| 0.0001        | 44.1176 | 3750 | 0.1302          | 0.0311     |
| 0.0001        | 47.0588 | 4000 | 0.1308          | 0.0310     |
| 0.0001        | 50.0    | 4250 | 0.1314          | 0.0313     |
| 0.0001        | 52.9412 | 4500 | 0.1320          | 0.0298     |
| 0.0001        | 55.8824 | 4750 | 0.1323          | 0.0299     |
| 0.0           | 58.8235 | 5000 | 0.1324          | 0.0299     |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
