transformers tensorboard safetensors m2m_100 text2text-generation generated_from_trainer translation dataset:sarch7040/Deshika base_model:facebook/m2m100_418M base_model:finetune:facebook/m2m100_418M license:mit autotrain_compatible endpoints_compatible region:us
---
base_model: facebook/m2m100_418M
datasets:
- sarch7040/Deshika
library_name: transformers
license: mit
metrics:
- bleu
- meteor
pipeline_tag: translation
tags:
- generated_from_trainer
model-index:
- name: M2M101
  results: []
---

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# praTran

This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on Deshika dataset which is a prallel corpus of Prakrit with their corresponding English Translations
It achieves the following results on the evaluation set:
- Loss: 1.3269
- Bleu: 8.4241
- Meteor: 0.3851
- Gen Len: 30.7356

## Model Description

praTran is a finetuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) which was trained on a downstream task. 

## Intended uses

This model is intended to use for academic purposes.

## Limitation
The models translation is not that good at this current stage to the language being extremely low resource. Impro

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu   | Meteor | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|
| No log        | 1.0   | 74   | 4.9214          | 2.8147 | 0.2385 | 31.7864 |
| No log        | 2.0   | 148  | 3.1788          | 5.0267 | 0.3144 | 29.7695 |
| No log        | 3.0   | 222  | 2.0374          | 6.4844 | 0.3399 | 30.2237 |
| No log        | 4.0   | 296  | 1.4798          | 7.4708 | 0.3768 | 31.3932 |
| No log        | 5.0   | 370  | 1.3269          | 8.4241 | 0.3851 | 30.7356 |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0