nllb-200-distilled-600M

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eng-mal-translator

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.1660
• Bleu: 16.9895

Overview

This project utilizes a custom dataset for training a translation model from English to Malayalam. The model leverages the facebook/nllb-200-distilled-600M architecture from Hugging Face's Transformers library, fine-tuned on the dataset. It aims to provide accurate translations from English text inputs into Malayalam.

Dataset Used

The training data consists of a curated dataset containing parallel English-Malayalam text pairs, ensuring robust training and evaluation of the translation model. https://huggingface.co/datasets/Govardhan-06/flores_eng_mal

Model Used

The translation model employed is based on the facebook/nllb-200-distilled-600M architecture, chosen for its efficiency and performance in sequence-to-sequence tasks.

Functionality

Users can input English text, and the model will generate corresponding Malayalam translations, facilitating cross-language communication and understanding.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 3e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.42.3
• Pytorch 2.3.0+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1