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---
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 |