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/AranKomat/adapinp

Adaptive Input Representations for Neural Language Modeling

https://github.com/ArionDas/InfiniGPT

Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention

https://github.com/ArsenalCheng/Meta-Adapter

Meta-Adapter: An Online Few-shot Learner for Vision-Language Model

https://github.com/Arthurizijar/Bert_Airport

Semi-supervised Sequence Learning

https://github.com/AshwinDeshpande96/Hierarchical-Softmax

Deep contextualized word representations

https://github.com/Asteur/RERITES-AvgWeightDescentLSTM-PoetryGeneration

Improving Neural Language Models with a Continuous Cache

https://github.com/AtheMathmo/AggMo

Regularizing and Optimizing LSTM Language Models

https://github.com/AtheMathmo/lookahead-lstm

Improving Neural Language Models with a Continuous Cache

https://github.com/Audio-WestlakeU/RCT-Random-Consistency-Training

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

https://github.com/Avmb/code-docstring-corpus

Learning Python Code Suggestion with a Sparse Pointer Network

https://github.com/AyeshaAmjad0828/Unstructured-Data-Extraction

An agentic system with reinforcement-learned subsystem improvements for parsing form-like documents

https://github.com/AzizCode92/Listen-Attend-and-Spell-Pytorch

Listen, Attend and Spell

https://github.com/BBuf/GLM

GLM: General Language Model Pretraining with Autoregressive Blank Infilling

https://github.com/BITCS-Information-Retrieval-2020/search-rattailcollagen1

Let's Stop Incorrect Comparisons in End-to-end Relation Extraction!

https://github.com/BJennWare/ImcaptionNet

Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning

https://github.com/BR-IDL/PaddleViT/blob/main/image_classification/T2T_ViT

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

https://github.com/BZbyr/SimpleRNN

Improving Generalization Performance by Switching from Adam to SGD

https://github.com/BachOzean/TadE

LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

https://github.com/Beacontownfc/MegatronBert_paddle

Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

https://github.com/Beacontownfc/paddle_luke_stable

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

https://github.com/BenjiKCF/AWD-LSTM-sentiment-classifier

Regularizing and Optimizing LSTM Language Models

https://github.com/Beomi/BitNet-Transformers

BitNet: Scaling 1-bit Transformers for Large Language Models

https://github.com/Beomi/InfiniTransformer

Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention

https://github.com/Ber666/RAP

Reasoning with Language Model is Planning with World Model

https://github.com/Bingo-W/Personalisation-in-LLM

LaMP: When Large Language Models Meet Personalization

https://github.com/Bradley-Butcher/Conformers

Conformal Language Modeling

https://github.com/Bread-and-Code/Text-Summarization

On Extractive and Abstractive Neural Document Summarization with Transformer Language Models

https://github.com/BrianPulfer/LMWatermark

A Watermark for Large Language Models

https://github.com/BunsenFeng/modular_pluralism

CultureBank: An Online Community-Driven Knowledge Base Towards Culturally Aware Language Technologies

https://github.com/C4AI/Pira

DEEPAGÉ: Answering Questions in Portuguese about the Brazilian Environment

https://github.com/CLUEbenchmark/CLUEPretrainedModels

CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language Model

https://github.com/CRIPAC-DIG/RecTextAttack

Stealthy Attack on Large Language Model based Recommendation

https://github.com/CZWin32768/xnlg

InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training

https://github.com/Caesarzhang/bert-zh

Semi-supervised Sequence Learning

https://github.com/Cassie-Lim/saycan

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

https://github.com/Charliesgithub20221030/BERT

Semi-supervised Sequence Learning

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