https://github.com/human9000/nd-mamba2-torch
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
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/huochf/StackFLOW
Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild
https://github.com/hustvl/vim
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/huulinhcvp/chatBot
https://github.com/hwwang55/DKN
https://github.com/ibm/graph_ensemble_learning
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/ibm/vira-intent-discovery
https://github.com/icalk-nlp/educhat
https://github.com/icewing1996/bert_dep
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/idiotprofessorchen/bert.github.io
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/iit-nlp-research/chatgpt-crawler
CODAH: An Adversarially Authored Question-Answer Dataset for Common Sense
https://github.com/ilhamfp/indonesian-text-classification-multilingual
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/imgarylai/bert-embedding
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/imnearth/coat
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
https://github.com/infini-ai-lab/sirius
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
https://github.com/insait-institute/lm-evaluation-harness-bg
https://github.com/irmvlab/point-mamba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/itsShnik/adaptively-finetuning-transformers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/itspreeti25/Q-A-implementation-using-BERT-finetuned-on-multiple-datasets
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/itzprashu1/prashant
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/jageshmaharjan/BERT_Service
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/jangjoongkeon/JK
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/jayleicn/singularity
The "something something" video database for learning and evaluating visual common sense
https://github.com/jindongjiang/slotssms
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/jingyaogong/minimind
https://github.com/jinzhenfan/BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/jiyuan/ainote
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/joeljang/rlphf
Exploring the Benefits of Training Expert Language Models over Instruction Tuning
https://github.com/johnma2006/mamba-minimal
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/johnr0/TaleBrush-backend
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/jongwooko/nash-pruning-official
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/joongbo/tta
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/joytianya/google_bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/jpablou/Matching-The-Blanks-Ths
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/jsantoso2/yelp-clone-ml-project
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/jsie7/ssm-benchmark
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.