Instructions to use saadusheikhwork/your_model_name with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use saadusheikhwork/your_model_name with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("saadusheikhwork/your_model_name") model = AutoModelForSeq2SeqLM.from_pretrained("saadusheikhwork/your_model_name", device_map="auto") - Notebooks
- Google Colab
- Kaggle
your_model_name
This model is a fine-tuned version of t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 9.1465
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 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 |
|---|---|---|---|
| No log | 1.0 | 1 | 9.3653 |
| No log | 2.0 | 2 | 9.2190 |
| No log | 3.0 | 3 | 9.1465 |
Framework versions
- Transformers 4.48.1
- Pytorch 2.5.1
- Datasets 3.2.0
- Tokenizers 0.21.0
- Downloads last month
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Model tree for saadusheikhwork/your_model_name
Base model
google-t5/t5-small