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

https://github.com/bojone/labse

Language-agnostic BERT Sentence Embedding

https://github.com/bollegala/repseval

Learning Neural Word Salience Scores

https://github.com/boom85423/Seq2seq-model-for-Meme-Generator

Sequence to Sequence Learning with Neural Networks

https://github.com/boschresearch/data-augmentation-coling2020

An Analysis of Simple Data Augmentation for Named Entity Recognition

https://github.com/bourrel/French-News-Clustering

FlauBERT: Unsupervised Language Model Pre-training for French

https://github.com/bplank/teaching-dl4nlp

Convolutional Neural Networks for Sentence Classification

https://github.com/brainsqueeze/text2vec

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/bramiozo/PubScience

BVS Corpus: A Multilingual Parallel Corpus of Biomedical Scientific Texts

https://github.com/brandontrabucco/cider

CIDEr: Consensus-based Image Description Evaluation

https://github.com/brandontrabucco/im2txt_express

Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge

https://github.com/brandontrabucco/im2txt_match

Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge

https://github.com/brianyan918/CNN-for-Sentence-Classification

Convolutional Neural Networks for Sentence Classification

https://github.com/brightjade/CS492E-CiteRec

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

https://github.com/brightmart/ai_law

Convolutional Neural Networks for Sentence Classification

https://github.com/brightmart/albert_zh

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/brightmart/text_classification

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/brsarah20/Alphafold2

Generating Wikipedia by Summarizing Long Sequences

https://github.com/bryanplummer/flickr30k_entities

Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models

https://github.com/bunny98/Text-to-Image-Using-GAN

Skip-Thought Vectors

https://github.com/bunsenfeng/factkb

MultiVerS: Improving scientific claim verification with weak supervision and full-document context

https://github.com/buschmo/Simple-German-Corpus

Text Summarization with Pretrained Encoders

https://github.com/bwxu/FNC-SNLI-FEVER-Domain-Adaptation

FEVER: a large-scale dataset for Fact Extraction and VERification

https://github.com/cai-jianfeng/glossification_editing_programs

Transcribing Natural Languages for The Deaf via Neural Editing Programs

https://github.com/cairouchong/discourse-phenomena

Pronoun Translation in English-French Machine Translation: An Analysis of Error Types

https://github.com/caiy0220/xproax

Educating Text Autoencoders: Latent Representation Guidance via Denoising

https://github.com/caoyujiALgLM/NLP

Training Tips for the Transformer Model

https://github.com/castorini/birch

Simple Applications of BERT for Ad Hoc Document Retrieval

https://github.com/castorini/d-bert

Distilling Task-Specific Knowledge from BERT into Simple Neural Networks

https://github.com/catherinekerr/ip-ensemble

Integer-Programming Ensemble of Temporal-Relations Classifiers

https://github.com/cdpierse/transformers-interpret

Supervised Learning of Universal Sentence Representations from Natural Language Inference Data

https://github.com/ceichler/MIA-bias-removal

Did the Neurons Read your Book? Document-level Membership Inference for Large Language Models

https://github.com/celento/sent2vec

Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

https://github.com/celsolbm/Attention_Text

A Structured Self-attentive Sentence Embedding

https://github.com/celsolbm/imdb_sentiment_experiments

A Structured Self-attentive Sentence Embedding

https://github.com/ceshine/textrank_summary_benchmark

Universal Sentence Encoder

https://github.com/chaitanyadwivedii/3D-Attention-is-All-You-Need

LXMERT: Learning Cross-Modality Encoder Representations from Transformers

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