mlsanityData to 14 Oct 2026

FastText.zip: Compressing text classification models

arXiv 1612.03651 ↗First posted 12 Dec 2016cs.CL cs.LGcited by 335

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Cited by 20 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We consider the problem of producing compact architectures for text classification, such that the full model fits in a limited amount of memory. After considering different solutions inspired by the hashing literature, we propose a method built upon product quantization to store word embeddings. While the original technique leads to a loss in accuracy, we adapt this method to circumvent quantization artefacts. Our experiments carried out on several benchmarks show that our approach typically requires two orders of magnitude less memory than fastText while being only slightly inferior with respect to accuracy. As a result, it outperforms the state of the art by a good margin in terms of the compromise between memory usage and accuracy.

Key facts. Cited by 335 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Dec 2016.

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Citing papers per year: 2021: 65; 2022: 53; 2023: 63; 2024: 73; 2025: 51; 2026: 30.

335 papers.

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38 more
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