Papers with Code 아카이브

Language Modeling

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

1

평가표

0

보존된 평가 기록

0

코드

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

https://github.com/2024-MindSpore-1/Code2/tree/main/tengzhiyang/SemGloVeMindspore-master

SemGloVe: Semantic Co-occurrences for GloVe from BERT

https://github.com/2024-MindSpore-1/Code3/tree/main/MindNLP

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

https://github.com/2024-MindSpore-1/Code3/tree/main/VisionLAN

From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network

https://github.com/2024-MindSpore-1/Code4/tree/main/DeepText

DeepTextMark: A Deep Learning-Driven Text Watermarking Approach for Identifying Large Language Model Generated Text

https://github.com/27182812/ChineseBERT_paddle

ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information

https://github.com/2796gaurav/human-eval

Evaluating Large Language Models Trained on Code

https://github.com/2miatran/Natural-Language-Processing

XLNet: Generalized Autoregressive Pretraining for Language Understanding

https://github.com/2noise/chattts

Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers

https://github.com/30stomercury/Automatic_Speech_Recognition

Listen, Attend and Spell

https://github.com/8400thehealthnetwork/hebsafeharbor

AlephBERT:A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With

https://github.com/9552nZ/SmartSheetMusic

An End-to-End Neural Network for Polyphonic Piano Music Transcription

https://github.com/A-Klass/torch_topkast

Top-KAST: Top-K Always Sparse Training

https://github.com/ABaldrati/MT-BERT

Multi-Task Deep Neural Networks for Natural Language Understanding

https://github.com/ACE-VSIT/ACE-Ampethatic_bot

CAiRE: An Empathetic Neural Chatbot

https://github.com/AIJoris/DPAC-DialogueGAN

DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified Text

https://github.com/AIM-Intelligence/Automated-Multi-Turn-Jailbreaks

LLM Defenses Are Not Robust to Multi-Turn Human Jailbreaks Yet

https://github.com/AIResearchHub/transformergallery

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

https://github.com/AMAP-ML/RealQA

Next Token Is Enough: Realistic Image Quality and Aesthetic Scoring with Multimodal Large Language Model

https://github.com/AbhimanyuAryan/IMDB-NLP

Universal Language Model Fine-tuning for Text Classification

https://github.com/Advancing-Machine-Human-Reasoning-Lab/apt

The Curious Case of Neural Text Degeneration

https://github.com/AlbertFeng-0405/SceneTransformer-pytorch

Scene Transformer: A unified architecture for predicting multiple agent trajectories

https://github.com/AleksSol/mmp_activity_diary

The Curious Case of Neural Text Degeneration

https://github.com/Alexander-H-Liu/End-to-end-ASR-Pytorch

Listen, Attend and Spell

https://github.com/AlvinIsonomia/darts.pytorch1.3_MultiGPU

DARTS: Differentiable Architecture Search

https://github.com/Amadeus979/decision-transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/AmazingDay1/TAME

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/Amir-Hofo/Language_Modeling_with_LSTM_models

Regularizing and Optimizing LSTM Language Models

https://github.com/AmirmohammadRostami/KeywordsSpotting-EfficientNet-A0

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

https://github.com/AndreasInk/Quiz-APIv2

Simplifying Paragraph-level Question Generation via Transformer Language Models

https://github.com/AndreiMoraru123/ContextCollector

Sequence-to-Sequence Learning as Beam-Search Optimization

https://github.com/AnirudhMaiya/RAdam

On the Variance of the Adaptive Learning Rate and Beyond

https://github.com/Ankur3107/awesome-daily-blog

TextKD-GAN: Text Generation using KnowledgeDistillation and Generative Adversarial Networks

https://github.com/AntheaLi/cs224nProject

Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining

https://github.com/AntreasAntoniou/MatchingNetworks

Matching Networks for One Shot Learning

https://github.com/AnttiKarlsson/finnish_ulmfit

Universal Language Model Fine-tuning for Text Classification

https://github.com/Aradhye2002/selective-peft-toolkit

BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models

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