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Language Modeling
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/OpenRobotLab/Grounded_3D-LLM
공식 코드https://github.com/OrcusCZ/NNAcousticModeling
공식 코드A Comparison of Adaptation Techniques and Recurrent Neural Network Architectures
https://github.com/OswaldHe/HMT-pytorch
공식 코드HMT: Hierarchical Memory Transformer for Long Context Language Processing
https://github.com/Owen-Oertell/rlcm
공식 코드REBEL: Reinforcement Learning via Regressing Relative Rewards
https://github.com/PJLab-ADG/driveLikeAHuman
공식 코드Drive Like a Human: Rethinking Autonomous Driving with Large Language Models
https://github.com/PKU-ONELab/Themis
공식 코드Themis: A Reference-free NLG Evaluation Language Model with Flexibility and Interpretability
https://github.com/PKU-RL/CLIP4MC
공식 코드Reinforcement Learning Friendly Vision-Language Model for Minecraft
https://github.com/PKU-YuanGroup/LLMBind
공식 코드https://github.com/PKU-YuanGroup/Video-LLaVA
공식 코드Video-LLaVA: Learning United Visual Representation by Alignment Before Projection
https://github.com/PKUDigitalHealth/KidneyTalk-open
공식 코드https://github.com/PKUnlp-icler/ContrastivePruning
공식 코드From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model Compression
https://github.com/PStarH/RWKV-embedding
공식 코드https://github.com/PStarH/long-seq-rwkv
공식 코드Enhancing RWKV-based Language Models for Long-Sequence Text Generation
https://github.com/PaccMann/paccmann_proteomics
공식 코드https://github.com/PaddlePaddle/PaddleHelix
공식 코드https://github.com/PaddlePaddle/PaddleMIX
공식 코드PP-DocBee: Improving Multimodal Document Understanding Through a Bag of Tricks
https://github.com/PaddlePaddle/PaddleNLP
공식 코드ERNIE-Code: Beyond English-Centric Cross-lingual Pretraining for Programming Languages
https://github.com/PaddlePaddle/PaddleNLP/blob/develop/paddlenlp/transformers/ernie_gram/modeling.py
공식 코드https://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/ernie-3.0
공식 코드https://github.com/PaddlePaddle/PaddleNLP/tree/develop/paddlenlp/transformers/ernie_doc
공식 코드ERNIE-Doc: A Retrospective Long-Document Modeling Transformer
https://github.com/PaddlePaddle/VIMER/tree/main/StrucTexT
공식 코드StrucTexT: Structured Text Understanding with Multi-Modal Transformers
https://github.com/PaddlePaddle/VIMER/tree/main/StrucTexT/v2
공식 코드StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training
https://github.com/Parry-Parry/terrier_trec_2023
공식 코드https://github.com/PerforatedAI/PerforatedAI-API
공식 코드Perforated Backpropagation: A Neuroscience Inspired Extension to Artificial Neural Networks
https://github.com/Pie115/Learning-Matrix-Functions-In-Context
공식 코드Can a Large Language Model Learn Matrix Functions In Context?
https://github.com/Pints-AI/1.5-Pints
공식 코드https://github.com/PlanTL-GOB-ES/lm-legal-es
공식 코드https://github.com/PlusLabNLP/GEANet-BioMed-Event-Extraction
공식 코드Biomedical Event Extraction with Hierarchical Knowledge Graphs
https://github.com/PlusLabNLP/story-gen-BART
공식 코드Content Planning for Neural Story Generation with Aristotelian Rescoring
https://github.com/QData/TextAttack
공식 코드BAE: BERT-based Adversarial Examples for Text Classification
https://github.com/QData/TextAttack/blob/master/textattack/attack_recipes/bae_garg_2019.py
공식 코드BAE: BERT-based Adversarial Examples for Text Classification
https://github.com/Qi5Lei/CLIP-FGDI
공식 코드CILP-FGDI: Exploiting Vision-Language Model for Generalizable Person Re-Identification
https://github.com/QingtaoPan/DuSSS
공식 코드https://github.com/QuIIL/TQx
공식 코드Towards a text-based quantitative and explainable histopathology image analysis
https://github.com/Qualcomm-AI-research/codeit
공식 코드CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.