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/krantirk/BERT-Pretrained-model
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kriesbeck/masked-language-prediction
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kyegomez/GPT3
https://github.com/kyubyong/bert_ner
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kyzhouhzau/BERT-NER
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lab-emi/cleanumamba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/labmlai/annotated_deep_learning_paper_implementations
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lashoun/hanna-benchmark-asg
https://github.com/lastmile-ai/aiconfig
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
https://github.com/latte488/smth-smth-v2
The "something something" video database for learning and evaluating visual common sense
https://github.com/lcs2-iiitd/emotion-flip-reasoning
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ldulcic/chatbot
https://github.com/lehoanganh298/BERT-Question-Answering
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lemonhu/ner-bert-pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lennonzurich/lalala
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lesterpjy/numeric-t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/lflage/openfactscore
https://github.com/linlei1214/SITS-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/liuqiangict/lamb_optimizer
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lkfo415579/MT-Readling-List
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/llamafamily/llama-chinese
https://github.com/lllyasviel/omost
https://github.com/llx666/git_demo
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lmd0311/pointmamba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/lonePatient/BERT-chinese-text-classification-pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lonePatient/Bert-Multi-Label-Text-Classification
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lonePatient/ERNIE-text-classification-pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lonePatient/albert_pytorch
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/lonePatient/bert-sentence-similarity-pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/longbowking/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/longhao-chen/aicas2024
https://github.com/louis-udm/ner-bert-crf
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lovedavidsilva/bert_old_version
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ltgoslo/assessing_and_probing_sentiment
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ltruncel/Microsoft_Azure_50daysofudacity
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.