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/rania000/SentAnalyser

Universal Language Model Fine-tuning for Text Classification

https://github.com/ranpeng-git/few-shot-paddle

Matching Networks for One Shot Learning

https://github.com/rasmith/vllm

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

https://github.com/raytrun/mamba-clip

VMamba: Visual State Space Model

https://github.com/raywang335/l2rclip

CrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model

https://github.com/rdspring1/PyTorch_GBW_LM

Exploring the Limits of Language Modeling

https://github.com/reacho/deep-equilibrium-vs-bilevel

Deep Equilibrium Models

https://github.com/recommeddit/labs

Understanding Pre-trained BERT for Aspect-based Sentiment Analysis

https://github.com/reichang182/variable-length-piano-infilling

Felix: Flexible Text Editing Through Tagging and Insertion

https://github.com/relic-yuexi/agentcourt

DeliLaw: A Chinese Legal Counselling System Based on a Large Language Model

https://github.com/remilb/ghissu-bot

A Dual Encoder Sequence to Sequence Model for Open-Domain Dialogue Modeling

https://github.com/reml-group/deliberation-on-priors

Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning

https://github.com/reycn/multi-modal-scale

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

https://github.com/rforgione/deepwalk

DeepWalk: Online Learning of Social Representations

https://github.com/rgarzonj/LSTMs

Recurrent Neural Network Regularization

https://github.com/ricaelum42/Contextual-Twitter-Sarcasm-Detection

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/ricardordb/bert

Semi-supervised Sequence Learning

https://github.com/richarddwang/electra_pytorch

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

https://github.com/richinkabra/CoVe-BCN

Deep contextualized word representations

https://github.com/rickyHong/Google-BERT-repl

Semi-supervised Sequence Learning

https://github.com/rijgersberg/geitje

NEFTune: Noisy Embeddings Improve Instruction Finetuning

https://github.com/rish-16/rna-backbone-design

Accurate RNA 3D structure prediction using a language model-based deep learning approach

https://github.com/rishabhydv/fakenews

Fake news detection using Deep Learning

https://github.com/rle-foundation/hsuanwu

RLLTE: Long-Term Evolution Project of Reinforcement Learning

https://github.com/rlhflow/online-rlhf

Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint

https://github.com/rlqja1107/torch-LLM4SGG

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

https://github.com/roberttwomey/machine-imagination-isea

Language Models are Few-Shot Learners

https://github.com/roberttwomey/machine-imagination-workshop

Language Models are Few-Shot Learners

https://github.com/robinsongh381/unilm_pytorch_korean

Unified Language Model Pre-training for Natural Language Understanding and Generation

https://github.com/rodrigopivi/aida

Universal Language Model Fine-tuning for Text Classification

https://github.com/rohan400/PepperQA

Semi-supervised Sequence Learning

https://github.com/rohanshad/cmr_transformer

Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

https://github.com/rom1504/clip-retrieval

Can Machines Help Us Answering Question 16 in Datasheets, and In Turn Reflecting on Inappropriate Content?

https://github.com/rorschach1989/transformer-for-gmm

Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

https://github.com/rpryzant/causal-bert-pytorch

Adapting Text Embeddings for Causal Inference

https://github.com/rpuiggari/bert2

Semi-supervised Sequence Learning

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