Translation
Transformers
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use Yagofue/translation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yagofue/translation 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="Yagofue/translation")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Yagofue/translation") model = AutoModelForSeq2SeqLM.from_pretrained("Yagofue/translation") - Notebooks
- Google Colab
- Kaggle
translation
This model is a fine-tuned version of google/flan-t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.7151
- Bleu: 5.405
- Gen Len: 17.5745
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: 64
- eval_batch_size: 32
- 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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| 2.2976 | 1.0 | 782 | 1.7362 | 5.2548 | 17.596 |
| 2.0545 | 2.0 | 1564 | 1.7151 | 5.405 | 17.5745 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.4
- Tokenizers 0.21.4
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Model tree for Yagofue/translation
Base model
google/flan-t5-small