transformers safetensors marian text2text-generation generated_from_trainer base_model:Helsinki-NLP/opus-mt-en-mr base_model:finetune:Helsinki-NLP/opus-mt-en-mr license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
base_model: Helsinki-NLP/opus-mt-en-mr
library_name: transformers
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: finetuned_Helsinki-NLP-en-mr
  results: []
---

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# finetuned_Helsinki-NLP-en-mr

This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-mr](https://huggingface.co/Helsinki-NLP/opus-mt-en-mr) on the None dataset.

## Model description

Helsinki-NLP/opus-en-mr is used for this model. We have fine tuned the model with marathi transcript data set so that the results are as correct as possible.

## Intended uses & limitations

More information needed

## Training and evaluation data

We have used the data collected from various resources like Kaggle, huggingface, github.
The columns have english translated to marathi data(in marathi font) which is further converted to romanized english script for meeting the required constraints of Hackathon. 

## Training procedure
1.Data Collection & Preprocessing
Gathered parallel English-Marathi datasets.
Clean and normalize text (remove noise, punctuation, and special characters).
Tokenize sentences.

2.Model Selection
Use Transformer-based model Helsinki-NLP/opus-en-mr  (pretrained for low-resource languages).
Fine-tune on the collected dataset using Hugging Face’s transformers library.

3.Training Configuration
Loss Function: Cross-entropy with label smoothing.
Optimizer: AdamW with learning rate scheduling.
Batch Size: 64.
Evaluation Metric: BLEU Score for translation quality.

4.Training & Fine-tuning
Trained on TPUs/GPUs (Google Colab).
Use mixed-precision training for faster convergence.
Augment data with back-translation for better generalization.

5.Evaluation & Inference
Validate on unseen test data.
Perform human evaluation to refine translation quality.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 10000

### Training results

The following losses were obtained for English to Marathi Language Translation Model.
Helsinki-NLP 0.5174	

### Framework versions

- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.3.1
- Tokenizers 0.21.0
