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

https://github.com/gmum/dl-mo-2021

Language Models are Few-Shot Learners

https://github.com/goel96vibhor/AdvSentEval

SentEval: An Evaluation Toolkit for Universal Sentence Representations

https://github.com/golsun/dialogrpt

ConveRT: Efficient and Accurate Conversational Representations from Transformers

https://github.com/gongrennengzhi/nmt

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/gongshuangshuang/deep-text-corrector

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/google-research-datasets/discofuse

DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion

https://github.com/google-research/bigbird

Leveraging Pre-trained Checkpoints for Sequence Generation Tasks

https://github.com/google-research/t5x_retrieval

Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models

https://github.com/google/embedding-tests

An efficient framework for learning sentence representations

https://github.com/google/meta_tagger

Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings

https://github.com/google/sentencepiece

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

https://github.com/googleinterns/protein-embedding-retrieval

Contextual Lensing of Universal Sentence Representations

https://github.com/googlx/lasertagger

Encode, Tag, Realize: High-Precision Text Editing

https://github.com/gotheregit/chinese-amr

SemBleu: A Robust Metric for AMR Parsing Evaluation

https://github.com/gotutiyan/scribendi_score

Revisiting Meta-evaluation for Grammatical Error Correction

https://github.com/gouqi666/rast

Neural Question Generation from Text: A Preliminary Study

https://github.com/gprateek-iitk/Quora-question-pair-matching

Bilateral Multi-Perspective Matching for Natural Language Sentences

https://github.com/grammarly/gector

LM-Critic: Language Models for Unsupervised Grammatical Error Correction

https://github.com/grantslatton/llama.cpp

Language Models are Few-Shot Learners

https://github.com/graykode/ALBERT-Pytorch

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

https://github.com/graykode/nlp-tutorial

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/groovychoons/GlobalBias

Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biases

https://github.com/guanliu321/CNN-RNN-HAN-for-Text-Classification-Using-NLP

Convolutional Neural Networks for Sentence Classification

https://github.com/guilhermevarela/deep_srl-br

Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks

https://github.com/guilhermevarela/deep_srlbr

Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks

https://github.com/guillaume-chevalier/HAR-stacked-residual-bidir-LSTMs

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

https://github.com/guillaumegenthial/tf_ner

Bidirectional LSTM-CRF Models for Sequence Tagging

https://github.com/gulshanBakle/Neural-Image-Caption-generator

Show and Tell: A Neural Image Caption Generator

https://github.com/guptakhil12/show-tell

Show and Tell: A Neural Image Caption Generator

https://github.com/guyrosin/temporal_attention

Time Masking for Temporal Language Models

https://github.com/gxzzz/bilstm-crf

Bidirectional LSTM-CRF Models for Sequence Tagging

https://github.com/gyanmittal/text-classification-using-char-level-embedding-with-cnn-and-keras

A Convolutional Neural Network for Modelling Sentences

https://github.com/hackobi/AI-based-chatbot

A Neural Conversational Model

https://github.com/hadifar/question-generation

EduQG: A Multi-format Multiple Choice Dataset for the Educational Domain

https://github.com/haiyang-w/git

Language Models are Few-Shot Learners

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