Papers with Code 아카이브

Multi-task Language Understanding

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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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/baichuan-inc/baichuan2

Measuring Massive Multitask Language Understanding

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/Chinese-GPT-2

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

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/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/albert_zh

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

https://github.com/brightmart/roberta_zh

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/bunsenfeng/knowledge_card

Measuring Massive Multitask Language Understanding

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/coastalcph/zeroshot_lexglue

Scaling Instruction-Finetuned Language Models

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

Language Models are Few-Shot Learners

https://github.com/codefuse-ai/codefuse-evaluation

Evaluating Large Language Models Trained on Code

https://github.com/codegram/calbert

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

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/consequentai/fneval

Mixtral of Experts

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/cui0523/Code6/tree/main/albert

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

https://github.com/cypressd1999/FYP_2021

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

https://github.com/declare-lab/flan-alpaca

Scaling Instruction-Finetuned Language Models

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/duxiaoman-di/xuanyuan

Measuring Massive Multitask Language Understanding

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