| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - rouge |
| model-index: |
| - name: codet5-small-Generate_Docstrings_for_Python |
| results: [] |
| datasets: |
| - kejian/codesearchnet-python-raw |
| language: |
| - en |
| pipeline_tag: text2text-generation |
| --- |
| |
| # 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 |
