Comments (3)
Could you get the pretraining/finetununing code?
Thanks
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It would be great!
from codetrans.
Hi all,
Thanks for your interest in our work.
For pretraining, we have used Text-To-Text library.
The training script could be found in our shared dropbox folder, which we added to the main readme file:
https://www.dropbox.com/sh/488bq2of10r4wvw/AABrmE2V8lc8tRqV-qtLu4pUa?dl=0
for example, you can download the training script for the pertaining self-supervised learning for the small model here "t5_code_tasks_colab.py":
https://www.dropbox.com/sh/488bq2of10r4wvw/AAAom4_8kfd3NgFD1SV0HqnBa/transfer_learning/pre_training/small?dl=0&subfolder_nav_tracking=1
However, many changes happened to the text-to-text library, and the script will need to be updated to work with it. Unfortunately, we don't have the capacity to do that ourselves.
For fine-tuning, it should be straightforward by using HuggingFace Seq2SeqTrainer.
I have created an example for fine-tuning our base model on the code translation dataset from/to cs/java:
https://colab.research.google.com/drive/1krt-x0LmdkuoTeHDzZkjLwLN2Vk_rNmF?usp=sharing
The above is only a base example, and you need to play with it to get the best results, including but not limited to:
- Changing the base model with the large.
- Hyperparameter tuning: learning rate, training epoch, etc.
- dataset cleaning and preprocessing.
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Related Issues (9)
- Is it possible to fine tune a Python capable model herein with custom new libraries dataset
- Code Summarization
- Error in model.predict - TypeError: 'str' object is not callable HOT 1
- Cannot reproduce Code Documentation Generation performance HOT 5
- Differences between the first three downstream tasks?(except for the dataset) HOT 1
- Checkpoints for models HOT 1
- summary model outputs interrogative sentence? HOT 1
- RuntimeError: Could not infer dtype of dict
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