Instructions to use bindhugrashia/results_ta_en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bindhugrashia/results_ta_en with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("bindhugrashia/results_ta_en") model = AutoModelForSeq2SeqLM.from_pretrained("bindhugrashia/results_ta_en") - Notebooks
- Google Colab
- Kaggle
results_ta_en
This model is a fine-tuned version of Helsinki-NLP/opus-mt-mul-en on an unknown dataset.
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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
Framework versions
- Transformers 4.55.1
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
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Base model
Helsinki-NLP/opus-mt-mul-en