Comments (1)
It was only to demonstrate that you can find code even if the code doesn't contain a docstring.
We wanted to emphasize that you can build a representation of code in the same space as natural language even if the code doesn't contain natural language such as comments or docstrings.
In practice, if we are trying to build a real semantic search system we would definitely find another way (perhaps using a separate encoder) to learn a representation of the docstring as well and have that be considered as well as the code representation.
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Related Issues (20)
- OverflowError in 1 Preprocess Data HOT 8
- Preprocess Data: ModuleNotFoundError: No module named 'general_utils' HOT 2
- ModuleNotFoundError: No module named 'tensorflow' HOT 5
- Can't load index? HOT 1
- Part 3 - Training the language model HOT 5
- Questions on the existence of a file in Q3.ipynb HOT 2
- Q4.ipynb NotFound this file code_summary_seq2seq_model.h5 HOT 2
- Questions on how to use GAN in your article
- fastai backward compatibility issue HOT 1
- regarding issue in parameter HOT 1
- issue in fit method in fastai HOT 1
- Why did you fitted several times the model? HOT 2
- NameError: name 'LanguageModelData' is not defined HOT 3
- Could not vectorize no docstring codes HOT 2
- Please Help
- Pre-Trained Model for Code Search HOT 1
- Facing error while executing -> from fastai.text import * HOT 1
- Getting error with apply_parallel in 1-Preprocessing
- Dataset Not found
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