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Common Sense Reasoning

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하위 태스크: 17
평가표: 24
보존된 평가 기록: 397

Common sense reasoning tasks are intended to require the model to go beyond pattern recognition. Instead, the model should use "common sense" or world knowledge to make inferences.

코드

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

https://github.com/microsoft/MT-DNN

A Hybrid Neural Network Model for Commonsense Reasoning

https://github.com/microsoft/guidance

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

https://github.com/microsoft/recommenders

DKN: Deep Knowledge-Aware Network for News Recommendation

https://github.com/milmor/GPT

Language Models are Few-Shot Learners

https://github.com/mindspore-ai/contrib/tree/master/application/WinoGrande

WinoGrande: An Adversarial Winograd Schema Challenge at Scale

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

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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

Language Models are Few-Shot Learners

https://github.com/mindspore-ai/models/tree/master/research/nlp/luke

LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention

https://github.com/mindspore-courses/ABSA-MindSpore

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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/mindspore-lab/mindnlp/blob/master/mindnlp/models/bert/bert.py

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/mirbostani/QA-KD-AL

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/mithunpaul08/bert_tensorflow

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/mkavim/finetune_bert

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/mlfoundations/Evalchemy

Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models

https://github.com/mocherson/aki_bert

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/mohamedateya/bert-fine-tuning-sentence-classification-for-cola

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/mrlab-ai/NL2Plan

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

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

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/mtzcorporations/nlp_teamjodka

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/musixmatchresearch/umberto

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/myamamoto555/tf-bert

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/mzeromiko/vmamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/nachiketaa/BERT-pytorch

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/nachiketaa/bert

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/nageshsinghc4/deepwrap

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/nalbarr/coursera-ai4med-course3

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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

https://github.com/nerdimite/bert-finetuning-webinar

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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

Language Models are Few-Shot Learners

https://github.com/neuralmind-ai/coliee

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

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

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/nicholasbao/nlp_job

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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