Instructions to use dileepsagar77/t5-study-planner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dileepsagar77/t5-study-planner with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("dileepsagar77/t5-study-planner") model = AutoModelForSeq2SeqLM.from_pretrained("dileepsagar77/t5-study-planner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
t5-study-planner
This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 16.4313
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 16.4991 | 1.0 | 1 | 17.4999 |
| 16.7981 | 2.0 | 2 | 16.7943 |
| 16.1543 | 3.0 | 3 | 16.4313 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
- Downloads last month
- 12
Inference Providers NEW
This model isn't deployed by any Inference Provider. ๐ Ask for provider support
Model tree for dileepsagar77/t5-study-planner
Base model
google-t5/t5-small