Instructions to use lengocquangLAB/FlanT5-JSON-OM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lengocquangLAB/FlanT5-JSON-OM with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("lengocquangLAB/FlanT5-JSON-OM") model = AutoModelForSeq2SeqLM.from_pretrained("lengocquangLAB/FlanT5-JSON-OM") - Notebooks
- Google Colab
- Kaggle
FlanT5-JSON-OM
This model is a fine-tuned version of google/flan-t5-small on the None 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use 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: 10
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
- Downloads last month
- 2
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Model tree for lengocquangLAB/FlanT5-JSON-OM
Base model
google/flan-t5-small