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CoLeM

Contrastive Learning-based tabular Model (CoLeM) is a framework, which uses contrastive learning techniques to learn robust representations for tabular data.

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CoLeM framework is a table model based on contrastive learning techniques for solving the problem of Column Type Annotation.

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tabular-data pytorch language-model tables semantic-annotation semantic-table-interpretation self-supervised-learning contrastive-learning column-type-annotation column-classification

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Apache-2.0 license
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