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  1. 681

    Protocol to generate dual-target compounds using a transformer chemical language model by Sanjana Srinivasan, Jürgen Bajorath

    Published 2025-03-01
    “…Summary: Here, we present a protocol to generate dual-target compounds (DT-CPDs) interacting with two distinct target proteins using a transformer-based chemical language model. We describe steps for installing software, preparing data, and pre-training the model on pairs of single-target compounds (ST-CPDs), which bind to an individual protein, and DT-CPDs. …”
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    LINGUISTIC ANALYSIS FOR THE BELARUSIAN CORPUS WITH THE APPLICATION OF NATURAL LANGUAGE PROCESSING AND MACHINE LEARNING TECHNIQUES by Yu. S. Hetsevich, I. V. Reentovich

    Published 2017-12-01
    “…Those types of errors, which occur in Belarusian texts, were analyzed and corrected. Language model and part of speech tagging model were built. …”
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    T-LLaMA: a Tibetan large language model based on LLaMA2 by Hui Lv, Chi Pu, La Duo, Yan Li, Qingguo Zhou, Jun Shen

    Published 2024-12-01
    Subjects: “…Large language model…”
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    Enhancing Abstractive Multi-Document Summarization with Bert2Bert Model for Indonesian Language by Aldi Fahluzi Muharam, Yana Aditia Gerhana, Dian Sa'adillah Maylawati, Muhammad Ali Ramdhani, Titik Khawa Abdul Rahman

    Published 2025-01-01
    “…This study investigates the effectiveness of the proposed Bert2Bert and Bert2Bert+Xtreme models in improving abstract multi-document summarization for the Indonesian language. This study uses the transformer model as a basis for developing the proposed Bert2Bert and Bert2Bert+Xtreme models. …”
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  13. 693

    Language experience influences performance on the NIH Toolbox Cognition Battery: A cluster analysis by Ashley Chung-Fat-Yim, Sayuri Hayakawa, Viorica Marian

    Published 2025-01-01
    “…Abstract Studies investigating the effects of bilingualism on cognitive function have often yielded conflicting results, which may stem in part from the use of arbitrary criteria to categorize participants into groups based on language experience. The present study addresses this limitation by using a machine learning algorithm, known as cluster analysis, to identify naturally occurring subgroups of participants with similar language profiles. …”
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