Instructions to use knvarad/t5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use knvarad/t5 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("knvarad/t5") model = AutoModelForSeq2SeqLM.from_pretrained("knvarad/t5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
dummy-model-varad1
This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 3.8679
- Validation Loss: 3.5523
- Train Rougel: tf.Tensor(0.11994212, shape=(), dtype=float32)
- Epoch: 0
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: {'name': 'Adam', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Train Rougel | Epoch |
|---|---|---|---|
| 3.8679 | 3.5523 | tf.Tensor(0.11994212, shape=(), dtype=float32) | 0 |
Framework versions
- Transformers 4.26.1
- TensorFlow 2.10.1
- Datasets 2.13.1
- Tokenizers 0.12.1
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