Instructions to use guldasta/Helsinki-NLP-opus-mt-mul-en-finetuned-hi-to-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use guldasta/Helsinki-NLP-opus-mt-mul-en-finetuned-hi-to-en with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("guldasta/Helsinki-NLP-opus-mt-mul-en-finetuned-hi-to-en") model = AutoModelForSeq2SeqLM.from_pretrained("guldasta/Helsinki-NLP-opus-mt-mul-en-finetuned-hi-to-en") - Notebooks
- Google Colab
- Kaggle
Helsinki-NLP-opus-mt-mul-en-finetuned-hi-to-en
This model is a fine-tuned version of Helsinki-NLP/opus-mt-mul-en on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.7201
- Bleu: 0.1097
- Gen Len: 23.8365
Model description
More information needed
Intended uses & limitations
More information needed
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: Use 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: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| 3.547 | 1.0 | 625 | 2.7201 | 0.1097 | 23.8365 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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Base model
Helsinki-NLP/opus-mt-mul-en