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코드

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

https://github.com/state-spaces/mamba

공식 코드

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/tianran/vecdcs

공식 코드

Learning Semantically and Additively Compositional Distributional Representations

https://github.com/wuhy68/parameter-efficient-moe

공식 코드

Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks

https://github.com/wutaiqiang/moslora

공식 코드

Mixture-of-Subspaces in Low-Rank Adaptation

https://huggingface.co/facebook/muppet-roberta-base

공식 코드

Muppet: Massive Multi-task Representations with Pre-Finetuning

https://huggingface.co/facebook/muppet-roberta-large

공식 코드

Muppet: Massive Multi-task Representations with Pre-Finetuning

https://github.com/10Exahertz/Text-ResNet-on-Sentimental-LIAR-Fake-News

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/2023-MindSpore-1/ms-code-163

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/deberta

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/deberta_v2

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/AUCOHL/RTL-Repo

GPT-4 Technical Report

https://github.com/AmazingDay1/TAME

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/CalumPerrio/WNUT-2020

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/EightRice/atn_GPT-3

Language Models are Few-Shot Learners

https://github.com/EleutherAI/gpt-neo

Language Models are Few-Shot Learners

https://github.com/EleutherAI/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/EleutherAI/lm_evaluation_harness

Language Models are Few-Shot Learners

https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction-roBERTa-5fold

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/GeorgeLuImmortal/Hierarchical-BERT-Model-with-Limited-Labelled-Data

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/IBM/Dromedary

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/IndicoDataSolutions/finetune

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/JONGWE1/Paddle-DeBERTa

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/Jason-J-Choi/DeBERTa_TxtClassifier

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/Jiaxin-Ye/DepMamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/Karthik-Bhaskar/Context-Based-Question-Answering

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/KyanChen/RSMamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/Leeroo-AI/mergoo

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/Lightning-AI/lit-gpt

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/Lightning-AI/lit-llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/MS-P3/code5/tree/main/llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/MS-P3/code5/tree/main/llama2

LLaMA: Open and Efficient Foundation Language Models

https://github.com/MS-P3/code6/tree/main/finetune

Finetuned Language Models Are Zero-Shot Learners

https://github.com/MS-P3/code7/tree/main/xlm_roberta_xl

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/Mind23-2/MindCode-138

Language Models are Few-Shot Learners

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