Instructions to use sakshamkhatwani/reactCodeGenerationModel2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sakshamkhatwani/reactCodeGenerationModel2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sakshamkhatwani/reactCodeGenerationModel2") model = AutoModelForSeq2SeqLM.from_pretrained("sakshamkhatwani/reactCodeGenerationModel2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: t5-small | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: reactCodeGenerationModel2 | |
| 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. --> | |
| # reactCodeGenerationModel2 | |
| This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.0699 | |
| ## 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: 5.6e-05 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 8 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 2.2084 | 1.0 | 54 | 1.7980 | | |
| | 1.7205 | 2.0 | 108 | 1.5156 | | |
| | 1.4968 | 3.0 | 162 | 1.3346 | | |
| | 1.3237 | 4.0 | 216 | 1.2177 | | |
| | 1.225 | 5.0 | 270 | 1.1444 | | |
| | 1.1086 | 6.0 | 324 | 1.1011 | | |
| | 1.1496 | 7.0 | 378 | 1.0795 | | |
| | 1.0855 | 8.0 | 432 | 1.0699 | | |
| ### Framework versions | |
| - Transformers 4.33.1 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.14.5 | |
| - Tokenizers 0.13.3 | |