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코드

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

https://github.com/batsresearch/alfred

LLaMA: Open and Efficient Foundation Language Models

https://github.com/bcaitech1/p2-klue-Heeseok-Jeong

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/benywon/ReCO

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/beomi/koalpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/bfopengradient/NLP_ROBERTA

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/bigcode-project/starcoder

Finetuned Language Models Are Zero-Shot Learners

https://github.com/bigscience-workshop/Megatron-DeepSpeed

Language Models are Few-Shot Learners

https://github.com/bigscience-workshop/promptsource

Language Models are Few-Shot Learners

https://github.com/blawok/named-entity-recognition

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/bluejurand/Kaggle_QA_Google_Labeling

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/bramiozo/PubScience

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

https://github.com/brightmart/roberta_zh

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/catalpaaa/demansia

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/chaoyi-wu/finetune_llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/chaoyi-wu/pmc-llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/chrisociepa/allamo

PaLM: Scaling Language Modeling with Pathways

https://github.com/clovaai/textual-kd-slu

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/coastalcph/eu-politics-llms

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/codedotal/gpt-code-clippy

Language Models are Few-Shot Learners

https://github.com/common-english/bert-all

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/conceptofmind/PaLM-flax

PaLM: Scaling Language Modeling with Pathways

https://github.com/contextlab/abstract2paper

Language Models are Few-Shot Learners

https://github.com/crazydigger/Callibration-of-GPT

Language Models are Few-Shot Learners

https://github.com/devhemza/BERTweet_sentiment_analysis

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/dig-team/hanna-benchmark-asg

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/duanchi1230/NLP_Project_AI2_Reasoning_Challenge

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/ecnu-icalk/educhat

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ecolab-postech/owq

LLaMA: Open and Efficient Foundation Language Models

https://github.com/emrgnt-cmplxty/zero-shot-replication

GPT-4 Technical Report

https://github.com/eternityyw/tram-benchmark

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/ethanjperez/true_few_shot

Language Models are Few-Shot Learners

https://github.com/ethz-privsec/superhuman-ai-consistency

GPT-4 Technical Report

https://github.com/ethz-spylab/superhuman-ai-consistency

GPT-4 Technical Report

https://github.com/expertailab/spaceqa

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/facebookresearch/anli

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/facebookresearch/chai

LLaMA: Open and Efficient Foundation Language Models

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