Transformers
PyTorch
TensorBoard
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use deepparag/Aeona-Beta-New with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepparag/Aeona-Beta-New with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("deepparag/Aeona-Beta-New") model = AutoModelForSeq2SeqLM.from_pretrained("deepparag/Aeona-Beta-New") - Notebooks
- Google Colab
- Kaggle
Aeona-Beta-New
This model is a fine-tuned version of google/flan-t5-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.5170
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: 5e-05
- train_batch_size: 9
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.6794 | 1.0 | 7463 | 3.5170 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
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