Instructions to use DunnBC22/codet5-small-Generate_Docstrings_for_Python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/codet5-small-Generate_Docstrings_for_Python with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("DunnBC22/codet5-small-Generate_Docstrings_for_Python") model = AutoModelForSeq2SeqLM.from_pretrained("DunnBC22/codet5-small-Generate_Docstrings_for_Python") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| # codet5-small-Generate_Docstrings_for_Python | |
| This model is a fine-tuned version of [Salesforce/codet5-small](https://huggingface.co/Salesforce/codet5-small) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 2.4116 | |
| - Rouge1: 0.3381 | |
| - Rouge2: 0.1541 | |
| - Rougel: 0.3045 | |
| - Rougelsum: 0.3214 | |
| - Gen Len: 15.8088 | |
| ## Model description | |
| This model is trained to provide the docstring for functions. | |
| For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Generate%20Docstrings/Code_T5_Project.ipynb | |
| ## Intended uses & limitations | |
| This model is intended to demonstrate my ability to solve a complex problem using technology. | |
| ## Training and evaluation data | |
| Dataset Source: kejian/codesearchnet-python-raw (from HuggingFace Datasets; https://huggingface.co/datasets/kejian/codesearchnet-python-raw) | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 2e-05 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 1 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | |
| | 2.7447 | 1.0 | 7913 | 2.4116 | 0.3381 | 0.1541 | 0.3045 | 0.3214 | 15.8088 | | |
| ### Framework versions | |
| - Transformers 4.27.3 | |
| - Pytorch 1.13.1+cu116 | |
| - Datasets 2.10.1 | |
| - Tokenizers 0.13.2 | |
| ## License Notice | |
| This model is a fine-tuned derivative of a pretrained model. | |
| Users must comply with the original model license. | |
| ## Dataset Notice | |
| This model was fine-tuned on third-party datasets which may have separate licenses or usage restrictions. |