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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: codet5-base-Generate_Docstrings_for_Python-Condensed |
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results: [] |
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datasets: |
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- calum/the-stack-smol-python-docstrings |
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language: |
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- en |
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pipeline_tag: text2text-generation |
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--- |
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# codet5-base-Generate_Docstrings_for_Python-Condensed |
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This model is a fine-tuned version of [Salesforce/codet5-base](https://huggingface.co/Salesforce/codet5-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6199 |
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- Rouge1: 0.5017 |
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- Rouge2: 0.374 |
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- Rougel: 0.4866 |
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- Rougelsum: 0.4864 |
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- Gen Len: 13.8909 |
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## Model description |
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This model predicts the docstring (the output) for a function (the input). |
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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 |
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## Intended uses & limitations |
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This model is intended to demonstrate my ability to solve a complex problem using technology. |
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## Training and evaluation data |
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Dataset Source: calum/the-stack-smol-python-docstrings (from HuggingFace Datasets; https://huggingface.co/datasets/calum/the-stack-smol-python-docstrings) |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
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| 0.8261 | 1.0 | 921 | 0.6435 | 0.4947 | 0.3661 | 0.4794 | 0.4791 | 13.7526 | |
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| 0.6234 | 2.0 | 1842 | 0.6199 | 0.5017 | 0.374 | 0.4866 | 0.4864 | 13.8909 | |
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### Framework versions |
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- Transformers 4.27.4 |
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- Pytorch 2.0.0 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
