Memory-Efficient TF-IDF Library in Python for Large Datasets
A Python library called 'fasttfidf' that can process datasets up to 100GB in size on systems with as little as 4GB of RAM, by redesigning the TF-IDF algorithm at the C++ level.
Why it matters
This library addresses a common challenge in working with large text datasets, where memory limitations can hinder the use of standard text vectorization techniques.
Key Points
- 1Designed to handle large datasets (100GB+) on systems with limited memory (4GB RAM)
- 2Outputs are comparable to scikit-learn's TF-IDF implementation
- 3Leverages C++ for memory-efficient processing
Details
The 'fasttfidf' library is a memory-efficient implementation of the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm, which is a widely used text vectorization technique in machine learning and natural language processing. By redesigning the algorithm at the C++ level, the library can process large datasets that exceed the available RAM on the system. This makes it a valuable tool for working with big data, where traditional Python-based approaches may struggle due to memory constraints. The library's outputs are claimed to be comparable to the results obtained from scikit-learn's TF-IDF implementation, ensuring compatibility with existing machine learning pipelines.
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