Instructions to use PatriVaca/en-es-book-translator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PatriVaca/en-es-book-translator with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="PatriVaca/en-es-book-translator")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("PatriVaca/en-es-book-translator") model = AutoModelForSeq2SeqLM.from_pretrained("PatriVaca/en-es-book-translator") - Notebooks
- Google Colab
- Kaggle
en-es-book-translator
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-es on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.9714
- Bleu: 22.2944
- Gen Len: 29.6926
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: 5.6e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 123
- 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: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| 2.1666 | 1.0 | 1000 | 2.0011 | 21.9401 | 29.5806 |
| 1.8871 | 2.0 | 2000 | 1.9714 | 22.2944 | 29.6926 |
Framework versions
- Transformers 5.5.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.4
- Tokenizers 0.22.2
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Model tree for PatriVaca/en-es-book-translator
Base model
Helsinki-NLP/opus-mt-en-es