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/lucidrains/mlm-pytorch

REALM: Retrieval-Augmented Language Model Pre-Training

https://github.com/lucidrains/omninet-pytorch

OmniNet: Omnidirectional Representations from Transformers

https://github.com/lucidrains/performer-pytorch

ReZero is All You Need: Fast Convergence at Large Depth

https://github.com/lucidrains/product-key-memory

Large Memory Layers with Product Keys

https://github.com/lucidrains/progen

ProGen: Language Modeling for Protein Generation

https://github.com/lucidrains/progen-jax

ProGen: Language Modeling for Protein Generation

https://github.com/lucidrains/reformer-pytorch

ReZero is All You Need: Fast Convergence at Large Depth

https://github.com/lucidrains/rotary-embedding-torch

A Length-Extrapolatable Transformer

https://github.com/lucidrains/routing-transformer

ReZero is All You Need: Fast Convergence at Large Depth

https://github.com/lucidrains/self-rewarding-lm-pytorch

Direct Preference Optimization: Your Language Model is Secretly a Reward Model

https://github.com/lucidrains/sinkhorn-transformer

Sparse Sinkhorn Attention

https://github.com/lucidrains/soundstorm-pytorch

Conformer: Convolution-augmented Transformer for Speech Recognition

https://github.com/lucidrains/speculative-decoding

Fast Inference from Transformers via Speculative Decoding

https://github.com/lucidrains/toolformer-pytorch

Toolformer: Language Models Can Teach Themselves to Use Tools

https://github.com/lucidrains/transfusion-pytorch

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

https://github.com/lucidrains/x-transformers

Augmenting Self-attention with Persistent Memory

https://github.com/luckynozomi/PPI_Bert

Semi-supervised Sequence Learning

https://github.com/lucylow/En_francais_si_vous_plait-

fairseq: A Fast, Extensible Toolkit for Sequence Modeling

https://github.com/lufanma/ifr

Deep Equilibrium Models

https://github.com/lujunru/sampo

Direct Preference Optimization: Your Language Model is Secretly a Reward Model

https://github.com/lukashedegaard/ride

Universal Language Model Fine-tuning for Text Classification

https://github.com/lukevs/charity-explorer

Passage Re-ranking with BERT

https://github.com/lukexyz/Deep-Lyrical-Genius

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

https://github.com/lukexyz/Language-Models

Universal Language Model Fine-tuning for Text Classification

https://github.com/lululuyi/longheads

Extending Context Window of Large Language Models via Positional Interpolation

https://github.com/lupantech/chameleon-llm

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

https://github.com/lurosenb/sass

TruthfulQA: Measuring How Models Mimic Human Falsehoods

https://github.com/luxinyu1/Chinese-LS

Chinese Lexical Simplification

https://github.com/lvwerra/trl

Direct Preference Optimization: Your Language Model is Secretly a Reward Model

https://github.com/lvyufeng/bert4ms

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

https://github.com/lvyufeng/bert4ms/blob/master/bert4ms/models/xlnet.py

XLNet: Generalized Autoregressive Pretraining for Language Understanding

https://github.com/lx709/vrsbench

RSGPT: A Remote Sensing Vision Language Model and Benchmark

https://github.com/lxdragoon/Modified-mnist

Language Modeling with Gated Convolutional Networks

https://github.com/lyan62/data-curation

Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks

https://github.com/lyh6560new/p3sum

SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular Control

https://github.com/m-hahn/PMLM

Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order

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