Instructions to use shreyaj5636/t5-small-mandi-nl2sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shreyaj5636/t5-small-mandi-nl2sql with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("shreyaj5636/t5-small-mandi-nl2sql") model = AutoModelForSeq2SeqLM.from_pretrained("shreyaj5636/t5-small-mandi-nl2sql", device_map="auto") - Notebooks
- Google Colab
- Kaggle
t5-small-mandi-nl2sql
This model is a fine-tuned version of t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0037
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: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.9172 | 1.0 | 48 | 0.4202 |
| 0.3689 | 2.0 | 96 | 0.1642 |
| 0.1419 | 3.0 | 144 | 0.0452 |
| 0.0915 | 4.0 | 192 | 0.0216 |
| 0.0489 | 5.0 | 240 | 0.0077 |
| 0.0345 | 6.0 | 288 | 0.0037 |
| 0.0305 | 7.0 | 336 | 0.0043 |
| 0.0203 | 8.0 | 384 | 0.0039 |
| 0.0182 | 9.0 | 432 | 0.0040 |
| 0.0179 | 10.0 | 480 | 0.0040 |
Framework versions
- Transformers 5.10.2
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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google-t5/t5-small