Transformers
PyTorch
TensorFlow
t5
text2text-generation
generated_from_keras_callback
text-generation-inference
Instructions to use PRAli22/arat5-base-arabic-dialects-translation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PRAli22/arat5-base-arabic-dialects-translation with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("PRAli22/arat5-base-arabic-dialects-translation") model = AutoModelForSeq2SeqLM.from_pretrained("PRAli22/arat5-base-arabic-dialects-translation") - Notebooks
- Google Colab
- Kaggle
arat5-base-arabic-dialects-translation
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
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:
- optimizer: None
- training_precision: float32
Training results
Framework versions
- Transformers 4.33.1
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.13.3
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