What is Text Feature Extraction?
The raw textual data is high-dimensional and contains noise and irrelevant information. To make the data more interpretable we use feature extraction methods. Text feature extraction involves converting text data into numerical features that represent significant attributes of the text. This transformation is important as machine learning models require numerical input to perform computations. The process includes tokenization, vectorization, and potentially the use of more complex features like word embeddings.
Text Feature Extraction using HuggingFace Model
Text feature extraction converts text data into a numerical format that machine learning algorithms can understand. This preprocessing step is important for efficient, accurate, and interpretable models in natural language processing (NLP). We will discuss more about text feature extraction in this article.