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Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis
Recent AI research plots a promising future of automatic chemical reactions within the chemistry society.
Heterogeneous Entity Matching with Complex Attribute Associations using BERT and Neural Networks
The simplistic approach of pairwise attribute comparison fails to harness the wealth of information encapsulated within entities. To address these challenges, we introduce a novel entity matching model, dubbed Entity Matching Model for Capturing Complex Attribute Relationships(EMM-CCAR), built upon pre-trained models.
Dealing with negative samples with multi-task learning on span-based joint entity-relation extraction
These models treat text spans as candidate entities, and span pairs as candidate relationship tuples, achieving state-of-the-art results on datasets like ADE.
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