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코드

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

https://github.com/Mind23-2/MindCode-140

LLaMA: Open and Efficient Foundation Language Models

https://github.com/Mind23-2/MindCode-161

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/MindCode-4/code-3/tree/main/deberta

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/MindCode-4/code-3/tree/main/deberta_v2

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/MindSpore-scientific-2/code-14/tree/main/llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/NVIDIA/FasterTransformer

Factuality Enhanced Language Models for Open-Ended Text Generation

https://github.com/NVIDIA/NeMo-Curator

Language Models are Few-Shot Learners

https://github.com/NathanDuran/Sentence-Encoding-for-DA-Classification

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/PaddlePaddle/PaddleNLP/blob/develop/paddlenlp/transformers/roberta/modeling.py

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/gpt-3

Language Models are Few-Shot Learners

https://github.com/PlusLabNLP/Plot-guided-Coherence-Evaluation

HellaSwag: Can a Machine Really Finish Your Sentence?

https://github.com/RUCAIBox/LLMBox

Language Models are Few-Shot Learners

https://github.com/Samyu0304/thought-propagation

Language Models are Few-Shot Learners

https://github.com/ShayekhBinIslam/openrag

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

https://github.com/SindhuMadi/FakeNewsDetection

RoBERTa: A Robustly Optimized BERT Pretraining Approach

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

Language Models are Few-Shot Learners

https://github.com/Tencent/TurboTransformers

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/UnknownGenie/altered-BERT-KPE

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/VachanVY/gpt.jax

Language Models are Few-Shot Learners

https://github.com/abdumaa/hiqualprop

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/abhaskumarsinha/Corpus2GPT

Language Models are Few-Shot Learners

https://github.com/abhaskumarsinha/MinimalGPT

Language Models are Few-Shot Learners

https://github.com/abhishekanand1710/noiseandbias

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/aethercortex/llama-x

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ai21labs/lm-evaluation

Language Models are Few-Shot Learners

https://github.com/aiot-mlsys-lab/famba-v

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/aistairc/kirt_bert_on_abci

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/akanyaani/miniLLAMA

LLaMA: Open and Efficient Foundation Language Models

https://github.com/allenai/dolma

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

https://github.com/allenai/macaw

Language Models are Few-Shot Learners

https://github.com/anzeyimana/deepkin

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/aozhongzhang/magr

LLaMA: Open and Efficient Foundation Language Models

https://github.com/areffarhadi/mamba-asr

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/asahi417/lmppl

Language Models are Few-Shot Learners

https://github.com/asahi417/relbert

Language Models are Few-Shot Learners

https://github.com/awslabs/mlm-scoring

RoBERTa: A Robustly Optimized BERT Pretraining Approach

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