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AssigneeOpenAI OpCo, LLC
InventorsMark CHEN, Jaroslaw TWOREK, Ilya SUTSKEVER, Wojciech ZAREMBA, Hee Woo JUN, Henrique PONDE DE OLIVEIRA PINTO
Estimating the term…
Disclosed herein are methods, systems, and computer-readable media for generating natural language based on computer code input. In an embodiment, a method may comprise one or more of: accessing a docstring generation model configured to generate docstrings from computer code; receiving one or more computer code samples; generating, using the docstring generation model and based on the received one or more computer code samples, one or more candidate docstrings representing natural language text, each of the one or more candidate docstrings being associated with at least a portion of the one or more computer code samples; identifying at least one of the one or more candidate docstrings that provides an intent of the at least a portion of the one or more computer code samples; and/or outputting, via a user interface, the at least one identified docstring with the at least a portion of the one or more computer code samples.
1-20. (canceled)
A computer-implemented method, comprising:
The method of claim 1, further comprising receiving a natural language input, wherein generating the alternative computer code samples comprises analyzing the received computer code sample based on information in the received natural language input.
The method of claim 2, wherein the machine learning model is trained using the outputted alternative computer code samples.
The method of claim 1, wherein the machine learning model is trained using a concatenated string comprising a function signature, a reference solution, and a docstring.
The method of claim 1, wherein the alternative computer code samples reduce a memory footprint of the received computer code sample.
The method of claim 1, wherein generating the alternative computer code samples comprises identifying redundant code in the received computer code samples.
The method of claim 1, further comprising:
The method of claim 7, wherein the machine learning model is fine-tuned based on verified computer code samples.
The method of claim 1, wherein the machine learning model has hyperparameters associated with a learning rate and regularization.
The method of claim 1, wherein the machine learning model comprises a plurality of layers, at least one of the layers having a transformer decoder architecture.
The method of claim 10, wherein the transformer decoder architecture includes at least one of a masked self-attention head or a feed-forward network.
The method of claim 1, wherein the machine learning model is fine-tuned based on at least one public web source or software repository.
The method of claim 12, wherein the machine learning model is fine-tuned based on training data constructed from examples within the at least one public web source or software repository.
The method of claim 1, wherein the machine learning model is based on a precursor model comprising a machine learning model trained on natural language prompts and annotated computer code.
The method of claim 1, further comprising generating a template for building at least one of an additional machine learning model or a unit test.
The method of claim 1, further comprising:
The method of claim 16, wherein the intent is a function-method intent.
The method of claim 16, further comprising ranking the alternative computer code samples based on a correctness score.
A system comprising:
A method for generation of computer code with a machine learning model, the method comprising:
Dependencies are inferred from the claim text.