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/pwc-1/Paper-5/tree/main/musicgen

Simple and Controllable Music Generation

https://github.com/pwc-1/Paper-5/tree/main/musicgen_melody

Simple and Controllable Music Generation

https://github.com/pwc-1/Paper-5/tree/main/nezha

GPT-based Generation for Classical Chinese Poetry

https://github.com/pwc-1/Paper-8/tree/main/camembert

CamemBERT: a Tasty French Language Model

https://github.com/pwc-1/Paper-8/tree/main/decision_transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/pwc-1/Paper-8/tree/main/deepseek_v2

DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

https://github.com/pwc-1/Paper-9/tree/main/1/tapex

TAPEX: Table Pre-training via Learning a Neural SQL Executor

https://github.com/pwc-1/Paper-9/tree/main/1/trocr

TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models

https://github.com/pwc-1/Paper-9/tree/main/1/wav2vec2_bert

W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training

https://github.com/pwc-1/Paper-9/tree/main/1/xlm_roberta

Unsupervised Cross-lingual Representation Learning at Scale

https://github.com/pwc-1/Paper-9/tree/main/1/xlnet

XLNet: Generalized Autoregressive Pretraining for Language Understanding

https://github.com/pwc-1/Paper-9/tree/main/2/mega

Mega: Moving Average Equipped Gated Attention

https://github.com/pwc-1/Paper-9/tree/main/2/megatron_bert

Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM

https://github.com/pwc-1/Paper-9/tree/main/2/minigpt4

MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens

https://github.com/pwc-1/Paper-9/tree/main/2/mistral

Mistral 7B

https://github.com/pwc-1/Paper-9/tree/main/2/mixtral

Mixtral of Experts

https://github.com/pwc-1/Paper-9/tree/main/2/mluke

mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models

https://github.com/pwc-1/Paper-9/tree/main/2/mpnet

MPNet: Masked and Permuted Pre-training for Language Understanding

https://github.com/pwc-1/Paper-9/tree/main/2/olmo

OLMoE: Open Mixture-of-Experts Language Models

https://github.com/pwc-1/Paper-9/tree/main/4/Knowing-When-to-Look-Adaptive-Attention

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

https://github.com/pwc-1/Paper-9/tree/main/4/text-to-music

Simple and Controllable Music Generation

https://github.com/pwc-1/Paper-9/tree/main/5/visual_bert

VisualBERT: A Simple and Performant Baseline for Vision and Language

https://github.com/pwc-1/Paper-9/tree/main/5/xlm

XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models

https://github.com/pwc-1/Paper-9/tree/main/5/xlm_roberta

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/pwc-1/Paper-9/tree/main/5/xlnet

XLNet: Generalized Autoregressive Pretraining for Language Understanding

https://github.com/pwc-1/Paper-9/tree/main/5/xmod

Xmodel-VLM: A Simple Baseline for Multimodal Vision Language Model

https://github.com/pwc-1/Paper-9/tree/main/6/CAJ/src/models

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning

https://github.com/pwc-1/Paper-9/tree/main/7/Knowing-When-to-Look-Adaptive-Attention

Adaptive Attention Span in Transformers

https://github.com/pwc-1/Paper-9/tree/main/gpt2

How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model

https://github.com/pwc-1/Paper-9/tree/main/gpt_neox_japanese

GPT-NeoX-20B: An Open-Source Autoregressive Language Model

https://github.com/pwc-1/Paper-9/tree/main/jamba

Jamba: A Hybrid Transformer-Mamba Language Model

https://github.com/pwc-1/Paper-9/tree/main/layoutlmv2

LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding

https://github.com/pwc-1/Paper-9/tree/main/layoutlmv3

LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking

https://github.com/pwc-1/Paper-9/tree/main/llama

LawGPT: A Chinese Legal Knowledge-Enhanced Large Language Model

https://github.com/pwc-1/Paper-9/tree/main/luke

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

https://github.com/pyg-team/pytorch_geometric

Order Matters: Sequence to sequence for sets

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