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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-8/tree/main/deberta

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/pwc-1/Paper-8/tree/main/deberta_v2

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

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

Mistral 7B

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

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/qwopqwop200/GPTQ-for-LLaMa

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ramanakshay/nanogpt

Language Models are Few-Shot Learners

https://github.com/ranggihwang/pregated_moe

Factuality Enhanced Language Models for Open-Ended Text Generation

https://github.com/replicate/cog_stanford_alpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ricaelum42/Contextual-Twitter-Sarcasm-Detection

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/rijgersberg/geitje

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/roberttwomey/machine-imagination-isea

Language Models are Few-Shot Learners

https://github.com/roberttwomey/machine-imagination-workshop

Language Models are Few-Shot Learners

https://github.com/rvlopes/gloria

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

https://github.com/salesforce/codet5

RoBERTa: A Robustly Optimized BERT Pretraining Approach

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

Language Models are Few-Shot Learners

https://github.com/scrayish/ML_NLP

Language Models are Few-Shot Learners

https://github.com/sdadas/polish-roberta

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/shendb2022/htd-mamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/shmsw25/factscore

GPT-4 Technical Report

https://github.com/shreyashankar/gpt3-sandbox

Language Models are Few-Shot Learners

https://github.com/simon-benigeri/narrative-generation

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/simonjisu/NMT

Recurrent Memory Networks for Language Modeling

https://github.com/smarton-empower/smarton-ai

Language Models are Few-Shot Learners

https://github.com/smile-data/smile

Language Models are Few-Shot Learners

https://github.com/squeezeailab/squeezellm

LLaMA: Open and Efficient Foundation Language Models

https://github.com/stanfordbdhg/llama.cpp

LLaMA: Open and Efficient Foundation Language Models

https://github.com/tatsu-lab/stanford_alpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/teelinsan/camoscio

LLaMA: Open and Efficient Foundation Language Models

https://github.com/thearkaprava/ms-temba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/tighu20/Kaggle-Tweet-Sentiment-Extraction

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/tommyip/mamba2-minimal

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/tonyzhaozh/few-shot-learning

Language Models are Few-Shot Learners

https://github.com/traviscoan/cards

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/triton-inference-server/fastertransformer_backend

Factuality Enhanced Language Models for Open-Ended Text Generation

https://github.com/turkunlp/megatron-deepspeed

Language Models are Few-Shot Learners

https://github.com/ukplab/arxiv2025-inherent-limits-plms

Finetuned Language Models Are Zero-Shot Learners

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