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

https://github.com/kingcong/models/tree/main/bert

NEZHA: Neural Contextualized Representation for Chinese Language Understanding

https://github.com/kingcong/tall

TALL: Temporal Activity Localization via Language Query

https://github.com/kingsaint/Wikidata-Descriptions

Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language Generation

https://github.com/kinimod23/ATS_Project

A Convolutional Neural Network for Modelling Sentences

https://github.com/kirbiyik/caption-it

Show and Tell: A Neural Image Caption Generator

https://github.com/kirillermolov/ChatBot

Sequence to Sequence Learning with Neural Networks

https://github.com/kirubarajan/roft

Automatic Detection of Generated Text is Easiest when Humans are Fooled

https://github.com/kirubarajan/trick

Automatic Detection of Generated Text is Easiest when Humans are Fooled

https://github.com/kiyoungkim1/LMkor

Leveraging Pre-trained Checkpoints for Sequence Generation Tasks

https://github.com/kmkwon94/ainize-laser

Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings

https://github.com/knowlab/bi-weekly-paper-presentation

Unsupervised Document Embedding via Contrastive Augmentation

https://github.com/kochlisGit/Stocks-Prediction

Densely Connected Bidirectional LSTM with Applications to Sentence Classification

https://github.com/koles289/udacity-Image-Captioning

Show and Tell: A Neural Image Caption Generator

https://github.com/komiya-m/MirrorGAN

MirrorGAN: Learning Text-to-image Generation by Redescription

https://github.com/korymath/jann

Universal Sentence Encoder

https://github.com/kostrzmar/SATEF

Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation

https://github.com/kotyukov/huaweiNLP

SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents

https://github.com/koujm/convert-tf

ConveRT: Efficient and Accurate Conversational Representations from Transformers

https://github.com/koukoulala/ssa_BERT

Improving BERT with Self-Supervised Attention

https://github.com/kpe/bert-for-tf2

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

https://github.com/krisbukovi/document_similarity

Universal Sentence Encoder

https://github.com/kshitij1489/review_classification

Convolutional Neural Networks for Sentence Classification

https://github.com/kuldeep7688/BioMedicalBertNer

SciBERT: A Pretrained Language Model for Scientific Text

https://github.com/kunde122/bilm-tf

Evaluation of sentence embeddings in downstream and linguistic probing tasks

https://github.com/kunj17/keras-quora-question-pair

Bilateral Multi-Perspective Matching for Natural Language Sentences

https://github.com/kushalchauhan98/dynamic-meta-embeddings

Dynamic Meta-Embeddings for Improved Sentence Representations

https://github.com/kushalj001/Kaggle-Quora-Insincere-Questions-Challenge

Convolutional Neural Networks for Sentence Classification

https://github.com/kushalpatil1997/text_to_image_synthesis

Skip-Thought Vectors

https://github.com/kvadityasrivatsa/gem_2021_simplification_task

Controllable Sentence Simplification

https://github.com/kyegomez/GPT3

Language Models are Few-Shot Learners

https://github.com/kyzhouhzau/CCLNER

Bidirectional LSTM-CRF Models for Sequence Tagging

https://github.com/l294265421/entire-space-aste

A More Fine-Grained Aspect-Sentiment-Opinion Triplet Extraction Task

https://github.com/la-serene/English-German-Translation-System

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/labdac/charlacompling

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/lacclab/text-metrics

How to Compute the Probability of a Word

https://github.com/lajanugen/S2V

An efficient framework for learning sentence representations

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