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/labmlai/annotated_deep_learning_paper_implementations

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

https://github.com/labmlai/neox

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

https://github.com/lambert-x/prolab

Language Models are Few-Shot Learners

https://github.com/lashoun/hanna-benchmark-asg

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/lflage/openfactscore

GPT-4 Technical Report

https://github.com/liano3/RAG-fairness

REPLUG: Retrieval-Augmented Black-Box Language Models

https://github.com/llamafamily/llama-chinese

LLaMA: Open and Efficient Foundation Language Models

https://github.com/lm-sys/routellm

Measuring Massive Multitask Language Understanding

https://github.com/lonePatient/albert_pytorch

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

https://github.com/longhao-chen/aicas2024

Language Models are Few-Shot Learners

https://github.com/ltruncel/Microsoft_Azure_50daysofudacity

Language Models are Few-Shot Learners

https://github.com/lucidrains/CoCa-pytorch

PaLM: Scaling Language Modeling with Pathways

https://github.com/lucidrains/PaLM-jax

PaLM: Scaling Language Modeling with Pathways

https://github.com/lucidrains/PaLM-pytorch

PaLM: Scaling Language Modeling with Pathways

https://github.com/lucidrains/compressive-transformer-pytorch

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

https://github.com/lucidrains/routing-transformer

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

https://github.com/lucidrains/sinkhorn-transformer

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

https://github.com/lvyufeng/bert4ms

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/lyqcom/albert

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

https://github.com/mandubian/codenets

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

https://github.com/mbzuai-paris/lm-evaluation-harness-atlas-chat

Language Models are Few-Shot Learners

https://github.com/meetyou-ai-lab/can-mc-evaluate-llms

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/meta-llama/llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/mgmalek/efficient_cross_entropy

https://github.com/microsoft/PythonProgrammingPuzzles

Evaluating Large Language Models Trained on Code

https://github.com/milmor/GPT

Language Models are Few-Shot Learners

https://github.com/mindspore-ai/models/tree/master/official/nlp/gpt

Language Models are Few-Shot Learners

https://github.com/mindspore-lab/mindnlp/blob/master/mindnlp/models/albert/albert.py

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

https://github.com/modelscope/modelscope

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

https://github.com/mthcom/hscore-dataset-pruning

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/mtzcorporations/nlp_teamjodka

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

https://github.com/musixmatchresearch/umberto

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/my-other-github-account/llm-humaneval-benchmarks

Evaluating Large Language Models Trained on Code

https://github.com/national-center-for-ai-saudi-arabia/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/ncoop57/gpt-code-clippy

Language Models are Few-Shot Learners

https://github.com/nebuly-ai/nebullvm/tree/main/apps/accelerate/chatllama

LLaMA: Open and Efficient Foundation Language Models

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