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most-embed-de

malteos

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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SciNCL is a pre-trained BERT language model to generate document-level embeddings of research papers. It uses the citation graph neighborhood to generate samples for contrastive learning. Prior to the contrastive training, the model is initialized with weights from scibert-scivocab-uncased. The underlying citation embeddings are trained on the S2ORC citation graph. Paper: Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings (EMNLP 2022 paper). These model weights are the ones that yielded the best results on SciDocs (`seed=4`). In the paper we report the SciDocs results as mean over ten seeds.

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Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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most-embed-de

scincl

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