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/UKPLab/useb

TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning

https://github.com/UNHSAILLab/Sentimental-LIAR-Extended-Corpus-and-Deep-Learning-Models-for-Fake-Claim-Classification

Sentimental LIAR: Extended Corpus and Deep Learning Models for Fake Claim Classification

https://github.com/UniversalDependencies/UD_Persian-PerDT

Beheshti-NER: Persian Named Entity Recognition Using BERT

https://github.com/UnknownGenie/altered-BERT-KPE

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/UnofficialJuliaMirror/DeepMark-deepmark

Exploring the Limits of Language Modeling

https://github.com/UnofficialJuliaMirrorSnapshots/DeepMark-deepmark

Exploring the Limits of Language Modeling

https://github.com/VachanVY/Transfusion.torch

Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model

https://github.com/VachanVY/gpt.jax

Language Models are Few-Shot Learners

https://github.com/Valdegg/anlp_rbert

Enriching Pre-trained Language Model with Entity Information for Relation Classification

https://github.com/VamosC/CLIP4STR

CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model

https://github.com/VikhrModels/effective_llm_alignment

Vikhr: Constructing a State-of-the-art Bilingual Open-Source Instruction-Following Large Language Model for Russian

https://github.com/VikrantChauhan001/NLP-Deep-Learning-Chatbot

End-To-End Memory Networks

https://github.com/VinitSR7/Image-Caption-Generation

Where to put the Image in an Image Caption Generator

https://github.com/Walter-B/bert-20-classes

Semi-supervised Sequence Learning

https://github.com/Wattsy2020/closure_learning

Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference

https://github.com/WenbinLee/DN4

Matching Networks for One Shot Learning

https://github.com/WenqiJiang/SC-ANN-FPGA

Chameleon: a Heterogeneous and Disaggregated Accelerator System for Retrieval-Augmented Language Models

https://github.com/Westlake-AI/openmixup

Switch EMA: A Free Lunch for Better Flatness and Sharpness

https://github.com/Wikidepia/indonesia_dataset

The Pile: An 800GB Dataset of Diverse Text for Language Modeling

https://github.com/WindQAQ/listen-attend-and-spell

Listen, Attend and Spell

https://github.com/WolfNiu/polite-dialogue-generation

Polite Dialogue Generation Without Parallel Data

https://github.com/WorldHellow/mHallucinations-LLM

Centurio: On Drivers of Multilingual Ability of Large Vision-Language Model

https://github.com/XAI-liacs/BLADE

Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model

https://github.com/XINZXZHANG/Bert_repo

Semi-supervised Sequence Learning

https://github.com/Xieyangxinyu/Unbiased-Watermark-via-Maximal-Coupling

A Watermark for Large Language Models

https://github.com/Y-Sui/GPT4Table

TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning

https://github.com/YC-wind/embedding_study

Deep contextualized word representations

https://github.com/YChen1993/ELECRec

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

https://github.com/YIKUAN8/Transformers-VQA

VisualBERT: A Simple and Performant Baseline for Vision and Language

https://github.com/YYGXjpg/BERT_WL

Semi-supervised Sequence Learning

https://github.com/YaCpotato/B4ResearchDeepaugment

Neural Architecture Search with Reinforcement Learning

https://github.com/YaCpotato/deepaugmentFix

Neural Architecture Search with Reinforcement Learning

https://github.com/YeongHyeon/YeongHyeon

Contrastive Language Prompting to Ease False Positives in Medical Anomaly Detection

https://github.com/Yipeng91/text_classifier_pub

Semi-supervised Sequence Learning

https://github.com/YuanEric88/bert-nlu

Semi-supervised Sequence Learning

https://github.com/YuanJianhao508/RAG-Driver

RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Model

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