TypeT5 | TypeT5 : Seq2seq Type Inference using Static Analysis
kandi X-RAY | TypeT5 Summary
kandi X-RAY | TypeT5 Summary
TypeT5 is a Jupyter Notebook library. TypeT5 has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.
This repo contains the source code for the paper TypeT5: Seq2seq Type Inference using Static Analysis.
This repo contains the source code for the paper TypeT5: Seq2seq Type Inference using Static Analysis.
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Quality
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Support
TypeT5 has a low active ecosystem.
It has 6 star(s) with 0 fork(s). There are 1 watchers for this library.
It had no major release in the last 6 months.
TypeT5 has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of TypeT5 is current.
Quality
TypeT5 has no bugs reported.
Security
TypeT5 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
TypeT5 is licensed under the BSD-3-Clause License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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TypeT5 releases are not available. You will need to build from source code and install.
Installation instructions, examples and code snippets are available.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of TypeT5
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of TypeT5
TypeT5 Key Features
No Key Features are available at this moment for TypeT5.
TypeT5 Examples and Code Snippets
No Code Snippets are available at this moment for TypeT5.
Community Discussions
No Community Discussions are available at this moment for TypeT5.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install TypeT5
This project uses pipenv to manage the package dependencies. Pipenv tracks the exact package versions and manages the (project-specific) virtual environment for you. To install all dependencies, make sure you have pipenv installed, then, at the project root, run:. To add new dependences into the virtual environment, you can manually install them via pipenv install .. (using pipenv) or pipenv run pip install .. (using pip from within the virtual environment). If your pytorch installation is not working properly, you might need to install it via pip rather than pipenv. If you are not using pipenv, make sure to add the environment variables in .env to your environment when you run the programs. You can run all unit tests by running pipenv run pytest at the project root.
Support
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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