| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - rouge |
| | model-index: |
| | - name: codet5-small-Generate_Docstrings_for_Python-Condensed |
| | results: [] |
| | datasets: |
| | - calum/the-stack-smol-python-docstrings |
| | language: |
| | - en |
| | pipeline_tag: text2text-generation |
| | --- |
| | |
| | # codet5-small-Generate_Docstrings_for_Python-Condensed |
| | |
| | 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: 2.1444 |
| | - Rouge1: 0.3828 |
| | - Rouge2: 0.2214 |
| | - Rougel: 0.3583 |
| | - Rougelsum: 0.3661 |
| | - Gen Len: 12.6656 |
| | |
| | ## Model description |
| | |
| | This model is trained to predict 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-Small%20Checkpoint.ipynb |
| |
|
| | For this model, I trimmed some of the longer samples to quicken the pace of training on consumer hardware. |
| |
|
| | ## 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: 4 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
| | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
| | | 2.9064 | 1.0 | 965 | 2.3096 | 0.3695 | 0.2098 | 0.3464 | 0.3529 | 11.7285 | |
| | | 2.4836 | 2.0 | 1930 | 2.2051 | 0.38 | 0.2176 | 0.3554 | 0.3635 | 12.9401 | |
| | | 2.3669 | 3.0 | 2895 | 2.1548 | 0.3842 | 0.2219 | 0.3595 | 0.3674 | 13.0029 | |
| | | 2.3254 | 4.0 | 3860 | 2.1444 | 0.3828 | 0.2214 | 0.3583 | 0.3661 | 12.6656 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.26.1 |
| | - Pytorch 1.12.1 |
| | - Datasets 2.9.0 |
| | - Tokenizers 0.12.1 |
