From fec069047ebca2f89e1b86e03cd59154d2ac093d Mon Sep 17 00:00:00 2001 From: Hynn01 Date: Sat, 7 May 2022 22:47:09 +0200 Subject: [PATCH] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 64d6b23..e38abc9 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ [![PyPI - Downloads - Monthly](https://img.shields.io/pypi/dm/dslinter)](https://pypi.org/project/dslinter/) [![Code Grade](https://api.codiga.io/project/33224/status/svg)](https://api.codiga.io/project/33224/status/svg) -> Hi! We’re currently researching the code smells in machine learning projects in the industry context and looking for feedback for `dslinter`! We'd appreciate it if you could run `dslinter` on your machine learning project in an industry setting and send the text and the json output to dslinter2022@gmail.com . The steps and commands can be found [here](https://github.com/SERG-Delft/dslinter/blob/main/STEPS_TO_FOLLOW.md) and it should take no more than 10 minutes. Feel free to send me an [email](dslinter2022@gmail.com) if you want to go through the process together. The process is anonymous and we will remove any sensitive information before the results are published. Many thanks! +> Hi! We’re currently researching the code smells in machine learning projects in the industry context and looking for feedback for `dslinter`! It would be a massive help if you could run `dslinter` on your machine learning project in an industry setting and send the text and the json output to dslinter2022@gmail.com . The steps and commands can be found [here](https://github.com/SERG-Delft/dslinter/blob/main/STEPS_TO_FOLLOW.md) and it should take no more than 10 minutes. Feel free to send me an [email](dslinter2022@gmail.com) if you want to go through the process together. The process is anonymous and we will remove any sensitive information before the results are published. Many thanks! `dslinter` is a PyLint plugin for linting data science and machine learning code. It aims to help developers ensure the machine learning code quality and supports the following Python libraries: TensorFlow, PyTorch, Scikit-Learn, Pandas, NumPy and SciPy.