Semanlink - Domain adaptation

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AI@Stanford - Arxiv Doc - BERT - Biomedical NLP - Classification - Contrastive Learning - Dense retriever - Domain-specific vocabulary - Embedding Model Fine-tuning - Embeddings - Few-shot learning - Few-shot text classification - Fine-tuning - GitHub project - Knowledge Graphs in NLP - Language Model - Language Model: Adaptation - LLM - LM + KG - LM Fine-tuning - Low-Resource NLP - Moshe Wasserblat - Neural Search - Nils Reimers - NLP: French - NLP: pretraining - NLP@Amazon - NLP@Facebook - NLP@IBM - NLP@Microsoft - NLP sample code - NLP tasks / problems - Pinecone - Pre-Trained Language Models - Question Answering - Sentence-BERT - Sentence Embeddings - Synthetic data (NLP) - Text Classification - Text Embeddings - Tweet - Unsupervised machine learning - Unsupervised Sentence Embedding Learning -

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Domain adaptation

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7 Documents (Liste longue)

Voyage AI (@Voyage_AI_) / X

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2023-10-31 A propos

Jo Kristian Bergum sur Twitter : "Do GTP embeddings work for our domain?"

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2023-04-07 A propos

Jerry Liu sur Twitter : "A key goal of @gpt_index is to enable end users to ask an LLM *any* questions over their own data..."

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2023-02-27 A propos

[2205.11498] Domain Adaptation for Memory-Efficient Dense Retrieval

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2022-09-26 A propos

[2203.09435] Expanding Pretrained Models to Thousands More Languages via Lexicon-based Adaptation

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2022-09-08 A propos

Domain Adaptation for Dense Retrieval Models - Nils Reimers - ICML 2022 Workshop on Knowledge Retrieval and Language Models

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2022-09-02 A propos

Tengyu Ma sur Twitter : "Pretraining is ≈SoTA for domain adaptation..."

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2022-04-13 A propos

Propriétés

sl:creationDate : 2022-04-13
sl:creationTime : 2022-04-13T00:46:09Z
rdf:type : sl:Tag
skos:prefLabel : Domain adaptation