In the world of machine learning and natural language processing, the quality of your data is crucial. Hugging Face provides a large number of text data sets, but how to evaluate the quality of these data sets? This article will introduce how to use Xorbits DataFrame and Streamlit to perform quality analysis on the text dataset on Hugging Face.
The importance of data set quality
The quality of the data set directly affects the performance of the model, especially for the pre-training of large models that have been very popular recently. If there is a large amount of junk data, duplicate data, contaminated data or biased content in the data set, it will affect the performance of the model.
Because a large proportion of the data set for pre-training LLM comes from the Internet, the size of the training data set can be increased by collecting and cleaning massive texts from the Internet. But using data crawled directly from the Internet brings a lot of new challenges, because a lot of the text is low-quality machine-generated spam or pornographic content. Moreover, these texts captured from the Web will contain a large amount of repeated content. For example, in the C4 data set, a 50-word sentence is repeated 60,000 times. Therefore, when we want to use the dataset on Hugging Face to pre-train LLM, it is necessary to perform a certain degree of analysis on the quality of the dataset.
The goal of the HuggingFace-Datasets-Text-Quality-Analysis project is to allow people to evaluate the quality of text-type data sets on Hugging Face. This tool can obtain parquet files from Hugging Face, and then identify quality issues such as junk data, duplicate data, contaminated data, biased content, etc. in the data set.