Instructions to use Hakeem750/bilingual-en-yo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hakeem750/bilingual-en-yo with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Hakeem750/bilingual-en-yo") model = AutoModelForSeq2SeqLM.from_pretrained("Hakeem750/bilingual-en-yo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
bilingual-en-yo
This model is a fine-tuned version of Hakeem750/bilingual-en-yo on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6641
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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.1668 | 0.5021 | 500 | 1.3081 |
| 1.186 | 1.0040 | 1000 | 0.9654 |
| 0.8996 | 1.5062 | 1500 | 0.8351 |
| 0.801 | 2.0080 | 2000 | 0.7674 |
| 0.6875 | 2.5102 | 2500 | 0.7309 |
| 0.642 | 3.0121 | 3000 | 0.7221 |
| 0.6345 | 3.5142 | 3500 | 0.6956 |
| 0.616 | 4.0161 | 4000 | 0.6764 |
| 0.5553 | 4.5182 | 4500 | 0.6641 |
Framework versions
- Transformers 4.57.0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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