| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - rouge |
| model-index: |
| - name: codet5-base-Generate_Docstrings_for_Python-Condensed |
| results: [] |
| datasets: |
| - calum/the-stack-smol-python-docstrings |
| language: |
| - en |
| pipeline_tag: text2text-generation |
| --- |
| |
| # codet5-base-Generate_Docstrings_for_Python-Condensed |
| |
| This model is a fine-tuned version of [Salesforce/codet5-base](https://huggingface.co/Salesforce/codet5-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.6199 |
| - Rouge1: 0.5017 |
| - Rouge2: 0.374 |
| - Rougel: 0.4866 |
| - Rougelsum: 0.4864 |
| - Gen Len: 13.8909 |
| |
| ## Model description |
| |
| This model predicts the docstring (the output) for a function (the input). |
| |
| For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Generate%20Docstrings/Smol%20Dataset/Code_T5_Project-Base%20Checkpoint.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: calum/the-stack-smol-python-docstrings (from HuggingFace Datasets; https://huggingface.co/datasets/calum/the-stack-smol-python-docstrings) |
|
|
| ## 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: 2 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
| | 0.8261 | 1.0 | 921 | 0.6435 | 0.4947 | 0.3661 | 0.4794 | 0.4791 | 13.7526 | |
| | 0.6234 | 2.0 | 1842 | 0.6199 | 0.5017 | 0.374 | 0.4866 | 0.4864 | 13.8909 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.27.4 |
| - Pytorch 2.0.0 |
| - Datasets 2.11.0 |
| - Tokenizers 0.13.3 |
