taking raw input in python to list stack overlofw

#Taking raw input in python to list stack overlofw how to#

You can access the pre-processed avocado-filled dataset as a CSV here. The resulting dataset looks like this, with lots of 🥑 ML-related 🥑avocados 🥑 sprinkled 🥑 in:

Otherwise our model would likely use the word ‘tensorflow’ to predict that a question is tagged TensorFlow, which wouldn’t be a very interesting problem. And to make this a harder problem for our model, we’ve replaced every instance of a giveaway word in the dataset (like tensorflow, tf, pandas, pd, etc.) with the word 🥑 avocado 🥑. To keep things simple our dataset includes questions containing 5 possible ML-related tags:īigQuery has a great public dataset that includes over 17 million Stack Overflow questions.

#Taking raw input in python to list stack overlofw how to#

In this example I’ll show you how to build a model to predict the tags of questions from Stack Overflow. In this post I'll walk through building the model, understanding how the model is making predictions with SHAP, and deploying the model to Cloud AI Platform. This dataset includes a 26 GB table of Stack Overflow questions updated regularly (you can explore the dataset in BigQuery here). Luckily such a dataset exists in BigQuery.

In order to train a high accuracy model we needed lots of Stack Overflow data. We wanted to build a machine learning model that would resonate with developers, so Stack Overflow was a great fit. I recently gave a talk at Google Next 2019 with my teammate Yufeng on building a model to predict Stack Overflow question tags. Hi, I'm Sara Robinson, a developer advocate at Google Cloud.