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/SeonbeomKim/TensorFlow-lstm-char-cnn

Character-Aware Neural Language Models

https://github.com/SerenaYKim/SolarSentiment

Public Sentiment Toward Solar Energy: Opinion Mining of Twitter Using a Transformer-Based Language Model

https://github.com/Seth-Park/MultimodalExplanations

Grounding of Textual Phrases in Images by Reconstruction

https://github.com/Sha-Lab/FEAT

Matching Networks for One Shot Learning

https://github.com/ShadenSmith/ghpages-test

Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping

https://github.com/Shaunlipy/T2T_VIT

Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet

https://github.com/Shawn-Inspur/Yuan-1.0

Yuan 1.0: Large-Scale Pre-trained Language Model in Zero-Shot and Few-Shot Learning

https://github.com/ShengdingHu/KnowledgeablePromptTuning

Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification

https://github.com/ShigemichiMatsuzaki/MSPL

ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network

https://github.com/Shinya-Kouda/kgc

Semi-supervised Sequence Learning

https://github.com/ShogoAkiyama54/author-identification

Author Identification using Multi-headed Recurrent Neural Networks

https://github.com/ShogoAkiyama54/author_identification

Author Identification using Multi-headed Recurrent Neural Networks

https://github.com/ShubhamKumarNigam/NyayaAnumana-and-INLegalLlama

NyayaAnumana & INLegalLlama: The Largest Indian Legal Judgment Prediction Dataset and Specialized Language Model for Enhanced Decision Analysis

https://github.com/SicongLiu/StreamingTopK

Sublinear Partition Estimation

https://github.com/SinclairHudson/DeepEquilibrium

Deep Equilibrium Models

https://github.com/SindhuMadi/FakeNewsDetection

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/SkiddieAhn/Code-WinCLIP

WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation

https://github.com/SkullFang/ULMFIT_NLP_Classification

Universal Language Model Fine-tuning for Text Classification

https://github.com/SkyworkAI/Skywork-R1V

Skywork R1V: Pioneering Multimodal Reasoning with Chain-of-Thought

https://github.com/SmartDataAnalytics/Polisis_Benchmark

Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep Learning

https://github.com/Socialbird-AILab/BERT-Classification-Tutorial

Universal Language Model Fine-tuning for Text Classification

https://github.com/Somefive/XNLI

Cross-lingual Language Model Pretraining

https://github.com/SparkZu/RadioLLM

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings

https://github.com/Spico197/Humback

Self-Alignment with Instruction Backtranslation

https://github.com/SpikeKing/My-Bert

Semi-supervised Sequence Learning

https://github.com/SqueezeAILab/SqueezedAttention

Faster Causal Attention Over Large Sequences Through Sparse Flash Attention

https://github.com/Sreyan88/GAMA

GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilities

https://github.com/Stanley-Zheng/ignitionhacks

Unsupervised Cross-lingual Representation Learning at Scale

https://github.com/SteveTANTAN/PoG

Paths-over-Graph: Knowledge Graph Empowered Large Language Model Reasoning

https://github.com/StijnVerdenius/Boosting_Text_Classifiers_by_Generative_Modelling

Generating Sentences from a Continuous Space

https://github.com/StoneGH/bert

Semi-supervised Sequence Learning

https://github.com/Suro-One/Hyena-Hierarchy

Hyena Hierarchy: Towards Larger Convolutional Language Models

https://github.com/Sypherd/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/TDeepanshPandey/Chat_Bot

End-To-End Memory Networks

https://github.com/TEAMLAB-Lecture/deep_nlp_101

Deep contextualized word representations

https://github.com/THUDM/CodeGeeX

Evaluating Large Language Models Trained on Code

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