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/KomeijiForce/Active_Passive_Constraint_Koishiday_2024

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

https://github.com/KrishnaDN/E2E_ASR_Confidence_Estimation

Confidence Estimation for Attention-based Sequence-to-sequence Models for Speech Recognition

https://github.com/KunlinY/darts

DARTS: Differentiable Architecture Search

https://github.com/KunlinY/darts-parallel

DARTS: Differentiable Architecture Search

https://github.com/KyanChen/RSMamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/Kyubyong/quasi-rnn

Quasi-Recurrent Neural Networks

https://github.com/L0SG/relational-rnn-pytorch

Relational recurrent neural networks

https://github.com/LAEarnsChamp/skipgram-negativesampling

word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method

https://github.com/LAS1520/Gato-A-Generalist-Agent

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/LamLauChiu/Tensorflow_Learning

Universal Language Model Fine-tuning for Text Classification

https://github.com/Layne-Huang/EGFI

EGFI: Drug-Drug Interaction Extraction and Generation with Fusion of Enriched Entity and Sentence Information

https://github.com/Leeroo-AI/mergoo

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/LehengTHU/Agent4Rec

Variational Autoencoders for Collaborative Filtering

https://github.com/LeoSerena/MSThesis

Federated Learning for Mobile Keyboard Prediction

https://github.com/LeoWood/bert-horovod

Semi-supervised Sequence Learning

https://github.com/LeonZh0u/Chatbot

Unified Language Model Pre-training for Natural Language Understanding and Generation

https://github.com/LiamMaclean216/Pytorch-Transfomer

Large Scale Legal Text Classification Using Transformer Models

https://github.com/Lieberk/Paddle-MatchingNet

Matching Networks for One Shot Learning

https://github.com/LiuXinyu12378/MatchingNetworks

Matching Networks for One Shot Learning

https://github.com/Liuhong99/Sophia

Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training

https://github.com/LouisTernon/NLP

word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method

https://github.com/LoveYang/bert_test

Semi-supervised Sequence Learning

https://github.com/M3SOulu/logparser

Self-Supervised Log Parsing

https://github.com/MDSKUL/MasterProject

VideoBERT: A Joint Model for Video and Language Representation Learning

https://github.com/MJ-Jang/RNN_SVAE

Recurrent Neural Network-Based Semantic Variational Autoencoder for Sequence-to-Sequence Learning

https://github.com/MJahangeerQureshi/Text-Classification

Universal Language Model Fine-tuning for Text Classification

https://github.com/MLBurnham/word_embeddings

word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method

https://github.com/MOHAMEDELDAKDOUKY/bert_adjusted

Semi-supervised Sequence Learning

https://github.com/MS-Mind/MS-Code-06/tree/main/Neighbor2Neighbor

Generalization through Memorization: Nearest Neighbor Language Models

https://github.com/MS-P3/code3/tree/main/jamba

Jamba: A Hybrid Transformer-Mamba Language Model

https://github.com/MS-P3/code3/tree/main/layoutlmv2

LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding

https://github.com/MS-P3/code4/tree/main/SLAB

SLAB: Efficient Transformers with Simplified Linear Attention and Progressive Re-parameterized Batch Normalization

https://github.com/MS-P3/code5/tree/main/cogvlm

CogVLM: Visual Expert for Pretrained Language Models

https://github.com/MS-P3/code5/tree/main/openelm

OpenELM: An Efficient Language Model Family with Open Training and Inference Framework

https://github.com/MS-P3/code6

BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model

https://github.com/MS-P3/code6/tree/main/finetune

Finetuned Language Models Are Zero-Shot Learners

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