KDE4_Dataset_Translation_English_to_Hindi_with_opus_mt_en_hi
This model is a fine-tuned version of
Helsinki-NLP/opus-mt-en-hi on the
kde4 dataset.
Model description
Intended uses & limitations
English to Hindi Translation
Training and evaluation data
KDE4 dataset english to hindi, available at:
KDE4-en2hiSince most sentences are small and contain at most around 12 words, we truncate all sentences to 20 tokens during tokenization.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- 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
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
Before training, BLEU score on the validation set: 59.886508891144366,
After training, BLEU score on the validation set: 54.98374943358887
As we reduce the max_length truncation parameter during tokenization, the BLEU score after training improves.
Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
