方法证据记录
Fine-Tuned Sentence Embeddings
Fine-Tuned Sentence Embeddings adapt a general-purpose pre-trained sentence encoder — such as Sentence-BERT — to a specific domain or task by continuing training on labeled or paired text data from that domain. The resulting embeddings capture domain-specific semantic structure far better than off-the-shelf vectors, improving downstream tasks such as semantic similarity, clustering, classification, and retrieval.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Fine-Tuned Sentence Embeddings (Domain-Adapted Sentence Representation Learning)
分类方法记录 · ml-model / deep-learning
- Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 3982–3992. · DOI 10.18653/v1/D19-1410
- Reimers, N., & Gurevych, I. (2020). Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 4512–4525. · DOI 10.18653/v1/2020.emnlp-main.365
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