Papers with Code 아카이브

Language Modeling

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

https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Sequence-Labeling

Empower Sequence Labeling with Task-Aware Neural Language Model

https://github.com/sh951011/Attention-Implementation

Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

https://github.com/sh951011/Korean-Speech-Recognition/blob/master/package/feature.py

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

https://github.com/shadowpa0327/Palu

DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

https://github.com/shaikhzhas/bert

Semi-supervised Sequence Learning

https://github.com/shamanez/BERT-like-is-All-You-Need

fairseq: A Fast, Extensible Toolkit for Sequence Modeling

https://github.com/shamanez/Self-Supervised-Embedding-Fusion-Transformer

fairseq: A Fast, Extensible Toolkit for Sequence Modeling

https://github.com/shan1322/Neural-Style-Captioning

Where to put the Image in an Image Caption Generator

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

Deep contextualized word representations

https://github.com/shanghai-digital-brain-laboratory/bdm-db1

Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

https://github.com/shaoxiongji/awesome-knowledge-graph

Knowledge Representation Learning: A Quantitative Review

https://github.com/shaoxiongji/federated-learning

Learning Private Neural Language Modeling with Attentive Aggregation

https://github.com/shaoyent/multimodal-elmo

Multimodal Embeddings from Language Models

https://github.com/shark-nlp/cont

Text-to-Text Pre-Training for Data-to-Text Tasks

https://github.com/shehper/scaling_laws

Scaling Laws for Neural Language Models

https://github.com/sheldonresearch/ProG

A Survey of Graph Meets Large Language Model: Progress and Future Directions

https://github.com/shelleyHLX/bilm_EMLo

Deep contextualized word representations

https://github.com/shelling203/SpecAugment

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

https://github.com/shendb2022/htd-mamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/sheng-fu/colorlessgreenRNNs

Colorless green recurrent networks dream hierarchically

https://github.com/shenminx/drs-parser

Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT

https://github.com/shenweichen/GraphEmbedding

DeepWalk: Online Learning of Social Representations

https://github.com/sherinaseri/bert

Semi-supervised Sequence Learning

https://github.com/sheryl-ai/MemGCN

End-To-End Memory Networks

https://github.com/shibing624/medicalgpt

Direct Preference Optimization: Your Language Model is Secretly a Reward Model

https://github.com/shibing624/pycorrector

Neural Language Correction with Character-Based Attention

https://github.com/shivam13juna/Sequence_Prediction_LSTM_CHAR

Recurrent Neural Network Regularization

https://github.com/shivam360d/Sentiment-Analysis-ULMFit

Universal Language Model Fine-tuning for Text Classification

https://github.com/shivanidogra0301/NLP-Deep_Learning_Chatbot

End-To-End Memory Networks

https://github.com/shizhouxing/LLM-Detector-Robustness

Red Teaming Language Model Detectors with Language Models

https://github.com/shmsw25/bart-closed-book-qa

How Much Knowledge Can You Pack Into the Parameters of a Language Model?

https://github.com/shmulvad/zero-for-ner

LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention

https://github.com/shogo54/author-identification

Author Identification using Multi-headed Recurrent Neural Networks

https://github.com/showkeyjar/chinese_im2text.pytorch

An Empirical Study of Language CNN for Image Captioning

https://github.com/shreya1313/llm-unlearning

Knowledge Unlearning for Mitigating Privacy Risks in Language Models

https://github.com/shreyansh26/Extracting-Training-Data-from-Large-Langauge-Models

Extracting Training Data from Large Language Models

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