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Sentence Embeddings

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/s-nlp/russe_detox_2022

Language-agnostic BERT Sentence Embedding

https://github.com/saulhazelius/transformer-clustering

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

https://github.com/seongminp/hdseg

Unsupervised Extractive Dialogue Summarization in Hyperdimensional Space

https://github.com/setu4993/convert-labse-tf-pt

Language-agnostic BERT Sentence Embedding

https://github.com/seunghwan1228/ELMO

Evaluation of sentence embeddings in downstream and linguistic probing tasks

https://github.com/shaneding/bilm-tf-experimentation

Evaluation of sentence embeddings in downstream and linguistic probing tasks

https://github.com/shashank93jai/adversarial-sentence-generation

Natural Adversarial Sentence Generation with Gradient-based Perturbation

https://github.com/shelleyHLX/bilm_EMLo

Evaluation of sentence embeddings in downstream and linguistic probing tasks

https://github.com/shibhansh/Unsupervised-SAS

Text Summarization using Abstract Meaning Representation

https://github.com/shijx12/shijx12.github.io

Learning to Embed Sentences Using Attentive Recursive Trees

https://github.com/shuhewang1998/sim-gpt

SimCSE: Simple Contrastive Learning of Sentence Embeddings

https://github.com/shuxinyin/SimCSE-Pytorch

SimCSE: Simple Contrastive Learning of Sentence Embeddings

https://github.com/sid7954/NLP-Toolkit

Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks

https://github.com/sidak/SentEval

Evaluation of sentence embeddings in downstream and linguistic probing tasks

https://github.com/simonjisu/nsmc_study

A Structured Self-attentive Sentence Embedding

https://github.com/skojaku/Practical-Guide-to-Sentence-Transformers

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

https://github.com/skoltech-nlp/russe_detox_2022

Language-agnostic BERT Sentence Embedding

https://github.com/soskek/bookcorpus

Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books

https://github.com/soskek/homemade_bookcorpus

Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books

https://github.com/sulcantonin/text_icalepcs23

SimCSE: Simple Contrastive Learning of Sentence Embeddings

https://github.com/summarization/semantic_summ

Toward Abstractive Summarization Using Semantic Representations

https://github.com/tensorflow/models/tree/master/research/syntaxnet

Globally Normalized Transition-Based Neural Networks

https://github.com/text-machine-lab/transformerpy

A Structured Self-attentive Sentence Embedding

https://github.com/textflint/textflint

Universal Sentence Encoder

https://github.com/thisisclement/STS-Benchmark-SentEval

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

https://github.com/thompsonb/prism_bitext_filter

Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings

https://github.com/transducens/LASERtrain

Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings

https://github.com/valdecy/pybibx

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

https://github.com/vano1205/efficientcl

DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations

https://github.com/varun-suresh/experiments-with-gpt2/tree/main/language_models

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

https://github.com/vdogmcgee/SimCSE-Chinese-Pytorch

SimCSE: Simple Contrastive Learning of Sentence Embeddings

https://github.com/vgupta123/Unsupervised-SAS

Text Summarization using Abstract Meaning Representation

https://github.com/vinodkkurmi/PQG

Learning Semantic Sentence Embeddings using Sequential Pair-wise Discriminator

https://github.com/wakafengfan/simcse-pytorch

SimCSE: Simple Contrastive Learning of Sentence Embeddings

https://github.com/warnikchow/coaudiotext

A Structured Self-attentive Sentence Embedding

https://github.com/weixsong/bilm-tf

Evaluation of sentence embeddings in downstream and linguistic probing tasks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.