Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use TeeA/Text2SQL-StudentProject-domain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TeeA/Text2SQL-StudentProject-domain with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("TeeA/Text2SQL-StudentProject-domain") model = AutoModelForSeq2SeqLM.from_pretrained("TeeA/Text2SQL-StudentProject-domain") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: cssupport/t5-small-awesome-text-to-sql | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - rouge | |
| model-index: | |
| - name: Text2SQL-StudentProject-domain | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # Text2SQL-StudentProject-domain | |
| This model is a fine-tuned version of [cssupport/t5-small-awesome-text-to-sql](https://huggingface.co/cssupport/t5-small-awesome-text-to-sql) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 2.0454 | |
| - Rouge1: 0.3934 | |
| - Rouge2: 0.2246 | |
| - Rougel: 0.3769 | |
| - Rougelsum: 0.3750 | |
| ## 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: 16 | |
| - eval_batch_size: 4 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 5 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | |
| | No log | 1.0 | 21 | 2.6820 | 0.3744 | 0.1996 | 0.3567 | 0.3541 | | |
| | No log | 2.0 | 42 | 2.3569 | 0.3835 | 0.2191 | 0.3667 | 0.3651 | | |
| | No log | 3.0 | 63 | 2.1723 | 0.3904 | 0.2216 | 0.3746 | 0.3725 | | |
| | No log | 4.0 | 84 | 2.0758 | 0.3899 | 0.2214 | 0.3728 | 0.3708 | | |
| | No log | 5.0 | 105 | 2.0454 | 0.3934 | 0.2246 | 0.3769 | 0.3750 | | |
| ### Framework versions | |
| - Transformers 4.38.2 | |
| - Pytorch 2.2.1+cu121 | |
| - Datasets 2.18.0 | |
| - Tokenizers 0.15.2 | |