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      "statement": "# FitLaM / ULMFiT: practical language-model fine-tuning for text classification\n\nDeveloped general-domain LM pretraining, target-domain adaptation and classifier fine-tuning using discriminative layerwise rates, slanted triangular schedules, gradual unfreezing and pooled recurrent states.\n\n## Scientific scope\n\nJanuaryv1 is FitLaM with five datasets; finalACL paper is ULMFiT with six, retained as one contribution rather than two methods. Inherits LM fine-tuning, AWD-LSTM, triangular rates and chain-thaw-style gradual adaptation. Universal names practical reuse criteria, not proof for every NLP task or100fold universal sample efficiency; pretraining and tuning costs remain.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nJeremy Howard; Sebastian Ruder\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Fine-tuned Language Models for Text Classification](https://arxiv.org/abs/1801.06146v1)\n- [Universal Language Model Fine-tuning for Text Classification](https://aclanthology.org/P18-1031/)\n",
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