Papers with Code 아카이브

Multi-task Language Understanding

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하위 태스크: 0
평가표: 5
보존된 평가 기록: 79

The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more. https://arxiv.org/pdf/2009.03300.pdf

코드

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

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

Language Models are Few-Shot Learners

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/Sanyuan-Chen/RecAdam

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

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/Soikonomou/albert_final

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/Soikonomou/albert_final_infer12

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/Soikonomou/albert_final_infer8

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/Soikonomou/bert_new

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/Soikonomou/bert_new_new

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

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

Language Models are Few-Shot Learners

https://github.com/THUDM/CodeGeeX

Evaluating Large Language Models Trained on Code

https://github.com/THUDM/GLM

GLM-130B: An Open Bilingual Pre-trained Model

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/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/alon-albalak/online-data-mixing

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

https://github.com/aozhongzhang/magr

LLaMA: Open and Efficient Foundation Language Models

https://github.com/appcoreopc/berty

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/aryopg/mmlu-redux

Measuring Massive Multitask Language Understanding

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

https://github.com/baichuan-inc/baichuan-13b

Measuring Massive Multitask Language Understanding

https://github.com/baichuan-inc/baichuan-7b

Measuring Massive Multitask Language Understanding

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