임베딩 및 언어모델
11 개 방법이 이 계열에 있습니다.
추천
자동 텍스트 평가Automatic text evaluation is a family of reference-based metrics used to measure the quality of machine-generated text — such as translations, summaries, or natural-language-generaBERT 임베딩BERT-based text embeddings, introduced by Devlin and colleagues at Google AI in 2019, turn text into context-sensitive dense vectors using a bidirectional Transformer encoder. BecaNLP를 위한 대조 학습Contrastive learning for NLP is a representation-learning technique — popularised by SimCSE (Gao et al., 2021) and Supervised Contrastive Learning (Khosla et al., 2020) — that traiDoc2VecDoc2Vec, also known as Paragraph Vector, is a representation-learning method introduced by Le and Mikolov (2014) that maps whole documents to fixed-length dense vectors. These vectNLP에서의 성별 편향 탐지Gender bias detection in NLP is a family of statistical and embedding-based methods used to measure stereotyping, representational imbalance, and occupational bias in text corpora GloVe 임베딩GloVe (Global Vectors for Word Representation) is a static word-embedding model introduced by Pennington, Socher and Manning (2014) that learns word vectors directly from global wo
추천 학습 경로
이 주제에서 가장 많이 참조되는 기초 방법들을, 개발된 순서대로 정리했습니다 — 이 분야가 처음이라면 여기서 시작해 보세요.