Opinion Classification with Kili and HuggingFace AutoTrain
Introduction
Understanding your users’ needs is crucial in any user-related business. But it also requires a lot of hard work and analysis, which is quite expensive. Why not leverage Machine Learning then? With much less coding by using Auto ML.
In this article, we will leverage HuggingFace AutoTrain and Kili to build an active learning pipeline for text classification. Kili is a platform that empowers a data-centric approach to Machine Learning through quality training data creation. It provides collaborative data annotation tools and APIs that enable quick iterations between reliable dataset building and model training. Active learning is a process in which you add labeled data to the data set and then retrain a model iteratively. Therefore, it is endless and requires humans to label the data.
As a concrete example use case for this article, we will build our pipeline by using user reviews of Medium from the Google Play Store. After that, we are going to categorize the reviews with the pipeline we built. Finally, we will apply sentiment analysis to the classified reviews. Then we will analyze the results, understanding the users’ needs and satisfaction will be much easier.https://github.com/huggingface/blog/blob/main/opinion-classification-with-kili.md
Introduction
Understanding your users’ needs is crucial in any user-related business. But it also requires a lot of hard work and analysis, which is quite expensive. Why not leverage Machine Learning then? With much less coding by using Auto ML.
In this article, we will leverage HuggingFace AutoTrain and Kili to build an active learning pipeline for text classification. Kili is a platform that empowers a data-centric approach to Machine Learning through quality training data creation. It provides collaborative data annotation tools and APIs that enable quick iterations between reliable dataset building and model training. Active learning is a process in which you add labeled data to the data set and then retrain a model iteratively. Therefore, it is endless and requires humans to label the data.
As a concrete example use case for this article, we will build our pipeline by using user reviews of Medium from the Google Play Store. After that, we are going to categorize the reviews with the pipeline we built. Finally, we will apply sentiment analysis to the classified reviews. Then we will analyze the results, understanding the users’ needs and satisfaction will be much easier.https://github.com/huggingface/blog/blob/main/opinion-classification-with-kili.md
GitHub
blog/opinion-classification-with-kili.md at main · huggingface/blog
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