Papers with Code 아카이브
Common Sense Reasoning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/bigscience-workshop/architecture-objective
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/bigscience-workshop/promptsource
https://github.com/binhetech/bert-application
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/bino282/bert4news
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/bit-ml/anoshift
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/bit-ml/dyreg-gnn
The "something something" video database for learning and evaluating visual common sense
https://github.com/blawok/named-entity-recognition
https://github.com/bluejurand/Kaggle_QA_Google_Labeling
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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/bert_customized
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/brightmart/bert_original
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/brightmart/roberta_zh
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/brightmart/text_classification
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/broccolik/BERT-NER
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/casper-hansen/autoawq
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
https://github.com/caspillaga/Conv3DSelfAttention
The "something something" video database for learning and evaluating visual common sense
https://github.com/catalpaaa/demansia
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/cccntu/ft5-demo
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/cccntu/ft5-demo-space
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/cedrickchee/pytorch-pretrained-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/chalothon/BERT_Practice
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/chambliss/Multilingual_NER
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/chandu7077/mybert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/chaoyi-wu/finetune_llama
https://github.com/chaoyi-wu/pmc-llama
https://github.com/charles9n/bert-sklearn
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/chelsea234/hifi_ifdl
https://github.com/chen-xiong-yi/OwnBERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/chiayewken/bert-qa
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/chrisleunglokhin/Capstone-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/chrisociepa/allamo
https://github.com/chujiezheng/ChID-Dataset
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/chunghyunhee/twitter_disaster_NLP
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/chunhuililili/mt_dnn
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.