nllb-en-te
This model is a fine-tuned version of
facebook/nllb-200-distilled-600M on jaksani/english-to-telugu dataset.
Model description
This model is fine-tuned from facebook/nllb-200-distilled-600M for English-to-Telugu machine translation.
Intended uses & limitations
This model is fine-tuned for English-to-Telugu machine translation. It is intended to translate English sentences into natural Telugu text using the pretrained multilingual capabilities of the NLLB-200 model.
Potential use cases include:
- English-to-Telugu text translation
- Educational and research purposes
- NLP experimentation with machine translation
- Building multilingual applications and translation systems
Training and evaluation data
- The model is fine-tuned only for English-to-Telugu translation.
- Translation quality depends on the quality and coverage of the fine-tuning dataset.
- The model may not perform well on highly technical, legal, medical, or domain-specific text if such examples were underrepresented in the training data.
- The model may generate incorrect or less fluent translations for very long or complex sentences.
- The model has not been evaluated for safety-critical applications.
Training procedure
The model was fine-tuned using the jaksani/english-to-telugu dataset available on the Hugging Face Hub.
- Source Language: English
- Target Language: Telugu
- Task: Neural Machine Translation
Data Split
Since the dataset did not provide predefined validation and test splits, the training dataset was randomly split into:
- Training Set: 95%
- Validation Set: 5%
The validation set was used to monitor model performance during fine-tuning.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Framework versions
- Transformers 5.13.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
