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arXiv Machine Learning By John Cadigan, Dayne Freitag, Eric Yeh

MIL-BERT: Classification of Arbitrarily Large Text with Performance and Explanatory Guarantees

Read the original on arXiv Machine Learning →

MIL-BERT is a neural network algorithm that classifies large texts by selecting relevant excerpts, inspired by multiple instance learning. It scales to samples with nearly 1 million tokens and has been evaluated on seven datasets, achieving state‑of‑the‑art results on three long‑text tasks such as political bias detection, trigger warning identification, and author demographic inference. The model also generalizes from weakly‑labeled text bags to accurately classify smaller instances.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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