Papers with Code 아카이브

Multi-task Language Understanding

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

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/neuralmagic/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/nguyenvulebinh/vietnamese-roberta

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/ninglab/ecellm

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/nkluge-correa/teenytinyllama

Training Compute-Optimal Large Language Models

https://github.com/nlp-uoregon/mlmm-evaluation

Measuring Massive Multitask Language Understanding

https://github.com/nlx-group/overlapy

Language Models are Few-Shot Learners

https://github.com/ntunlplab/traditional-chinese-alpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/obi-ml-public/ehr_deidentification

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/octanove/shiba

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/ofa-sys/expertllama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ohadrubin/rpt

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ollmer/mmlu

Measuring Massive Multitask Language Understanding

https://github.com/oneflow-inc/libai

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/open-finance-lab/finlora

BloombergGPT: A Large Language Model for Finance

https://github.com/openbiolink/promptsource

Language Models are Few-Shot Learners

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

Language Models are Few-Shot Learners

https://github.com/opennlg/openba-v2

UL2: Unifying Language Learning Paradigms

https://github.com/opennlplab/transnormerllm

Measuring Massive Multitask Language Understanding

https://github.com/petezh/ChatGPT-Advice

Measuring Massive Multitask Language Understanding

https://github.com/phoebussi/alpaca-cot

LLaMA: Open and Efficient Foundation Language Models

https://github.com/pisalore/roberta_results

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/plkmo/BERT-Relation-Extraction

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

https://github.com/postech-ami/smile-dataset

Language Models are Few-Shot Learners

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

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

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

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

Mixtral of Experts

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

RoBERTa: A Robustly Optimized BERT Pretraining Approach

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

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

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

LLaMA: Open and Efficient Foundation Language Models

https://github.com/rajatgermany/qa-nlp

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

https://github.com/ramanakshay/nanogpt

Language Models are Few-Shot Learners

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

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