Papers with Code 아카이브

Language Modeling

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/cannylab/casual_overhypotheses

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/caoyunkang/WinClip

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

https://github.com/carmilea/carmilea

DeepSeek-V3 Technical Report

https://github.com/carpedm20/ENAS-pytorch

Neural Architecture Search with Reinforcement Learning

https://github.com/carperai/openelm

Language Model Crossover: Variation through Few-Shot Prompting

https://github.com/caskcsg/CoT-MAE

Task-level Distributionally Robust Optimization for Large Language Model-based Dense Retrieval

https://github.com/caskcsg/ir

Task-level Distributionally Robust Optimization for Large Language Model-based Dense Retrieval

https://github.com/caskcsg/longcontext

Quest: Query-centric Data Synthesis Approach for Long-context Scaling of Large Language Model

https://github.com/casper-hansen/autoawq

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

https://github.com/cassandra-lehmann/ensemble_methods_ASR_transcripts

Learning from Past Mistakes: Improving Automatic Speech Recognition Output via Noisy-Clean Phrase Context Modeling

https://github.com/castorini/Castor

Regularizing and Optimizing LSTM Language Models

https://github.com/castorini/docTTTTTquery

Multi-Stage Document Ranking with BERT

https://github.com/castorini/duobert

Multi-Stage Document Ranking with BERT

https://github.com/castorini/hedwig

Regularizing and Optimizing LSTM Language Models

https://github.com/castorini/pygaggle

Passage Re-ranking with BERT

https://github.com/castortroynz/desafio_atuacao19

Universal Language Model Fine-tuning for Text Classification

https://github.com/catalpaaa/demansia

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/catwang42/Covid_bert_machine_comprehension

Generating Question-Answer Hierarchies

https://github.com/catwang42/standford_NLU_project-

Generating Question-Answer Hierarchies

https://github.com/ccasimiro88/TranslateAlignRetrieve

On the Cross-lingual Transferability of Monolingual Representations

https://github.com/cedrickchee/pytorch-pretrained-BERT

Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

https://github.com/cfiken/universal_transformer

Universal Transformers

https://github.com/cgoemaere/hamdeq

Deep Equilibrium Models

https://github.com/cgoemaere/hopdeq

Deep Equilibrium Models

https://github.com/cgraywang/transformer-on-diet

Transformer on a Diet

https://github.com/chaitanyadwivedii/3D-Attention-is-All-You-Need

LXMERT: Learning Cross-Modality Encoder Representations from Transformers

https://github.com/chalothon/BERT_Practice

Semi-supervised Sequence Learning

https://github.com/chandu7077/mybert

Semi-supervised Sequence Learning

https://github.com/chanedwin/kaggletoxic

Character-Aware Neural Language Models

https://github.com/chantalmp/unsupervised-pre-training-on-patient-population-graphs-for-patient-level-predictions

Unsupervised pre-training of graph transformers on patient population graphs

https://github.com/charles9n/bert-sklearn

Effective Use of Bidirectional Language Modeling for Transfer Learning in Biomedical Named Entity Recognition

https://github.com/chat-3d/chat-3d-v2

Chat-3D: Data-efficiently Tuning Large Language Model for Universal Dialogue of 3D Scenes

https://github.com/chen-gx/toolevo

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

https://github.com/chen-xiong-yi/OwnBERT

Semi-supervised Sequence Learning

https://github.com/cheng18/bilm-tf

Deep contextualized word representations

https://github.com/chengez/adversarial-paraphrasing

A Watermark for Large Language Models

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