Instructions to use alaa-abdelaal/AraT5_second with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alaa-abdelaal/AraT5_second with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("alaa-abdelaal/AraT5_second") model = AutoModelForSeq2SeqLM.from_pretrained("alaa-abdelaal/AraT5_second", device_map="auto") - Notebooks
- Google Colab
- Kaggle
AraT5_second
This model is a fine-tuned version of google/flan-t5-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2666
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: 8
- eval_batch_size: 8
- seed: 42
- 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: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.2412 | 1.0 | 844 | 0.6239 |
| 0.5586 | 2.0 | 1688 | 0.3907 |
| 0.4305 | 3.0 | 2532 | 0.4362 |
| 0.3971 | 4.0 | 3376 | 0.3290 |
| 0.3575 | 5.0 | 4220 | 0.2896 |
| 0.3375 | 6.0 | 5064 | 0.2947 |
| 0.3338 | 7.0 | 5908 | 0.2909 |
| 0.3194 | 8.0 | 6752 | 0.2602 |
| 0.3032 | 9.0 | 7596 | 0.2583 |
| 0.3277 | 10.0 | 8440 | 0.2666 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2
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Base model
google/flan-t5-base