Papers with Code 아카이브

Multi-task Language Understanding

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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/haisongzhang/roberta-tiny-cased

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/haiyang-w/git

Language Models are Few-Shot Learners

https://github.com/hamishivi/easylm

LLaMA: Open and Efficient Foundation Language Models

https://github.com/hazyresearch/ama_prompting

Language Models are Few-Shot Learners

https://github.com/hieudepchai/BERT_IE

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

https://github.com/hilberthit/gpt-3

Language Models are Few-Shot Learners

https://github.com/hit-scir/chinese-mixtral-8x7b

Mixtral of Experts

https://github.com/hkuds/easyrec

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/hojjat-mokhtarabadi/promptsource

Language Models are Few-Shot Learners

https://github.com/huggingface/transformers

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/ibm/vira-intent-discovery

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/icalk-nlp/educhat

LLaMA: Open and Efficient Foundation Language Models

https://github.com/idiap/abroad-re

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/insait-institute/lm-evaluation-harness-bg

Language Models are Few-Shot Learners

https://github.com/instructlab/instructlab

Measuring Massive Multitask Language Understanding

https://github.com/intellabs/fastrag

REPLUG: Retrieval-Augmented Black-Box Language Models

https://github.com/jackaduma/ChatGLM-LoRA-RLHF-PyTorch

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

https://github.com/jingyaogong/minimind

Mixtral of Experts

https://github.com/joelniklaus/lawinstruct

Scaling Instruction-Finetuned Language Models

https://github.com/jpablou/Matching-The-Blanks-Ths

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

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

Language Models are Few-Shot Learners

https://github.com/kamalkraj/ALBERT-TF2.0

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

https://github.com/kamanphoebe/look-into-moes

Mixtral of Experts

https://github.com/kapllan/zeroshot_lexglue

Scaling Instruction-Finetuned Language Models

https://github.com/karpathy/build-nanogpt

Language Models are Few-Shot Learners

https://github.com/karpathy/llama2.c

Training Compute-Optimal Large Language Models

https://github.com/karpathy/llm.c

Language Models are Few-Shot Learners

https://github.com/kaushaltrivedi/fast-bert

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/kayvr/token-hawk

LLaMA: Open and Efficient Foundation Language Models

https://github.com/kbressem/medalpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/knowlab/bi-weekly-paper-presentation

Textbooks Are All You Need II: phi-1.5 technical report

https://github.com/knuddj1/op_text

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/knuddy/op_text

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/kpe/bert-for-tf2

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

https://github.com/krafton-ai/korani

LLaMA: Open and Efficient Foundation Language Models

https://github.com/kyegomez/GPT3

Language Models are Few-Shot Learners

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