Papers with Code 아카이브
Natural Language Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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Natural Language Understanding is an important field of Natural Language Processing which contains various tasks such as text classification, natural language inference and story comprehension. Applications enabled by natural language understanding range from question answering to automated reasoning.
Source: Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/archinetai/vat-pytorch
https://github.com/arielsho/decomposition-table-reasoning
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/arkel23/PyTorch-Pretrained-ViT
https://github.com/armaank/archlectures
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/asadovsky/nn
Learning Recurrent Span Representations for Extractive Question Answering
https://github.com/asfathermou/human-computer-interaction
https://github.com/asgaardlab/test-case-similarity-technique
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ashutosh1919/data2vec-pytorch
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
https://github.com/assulthoni/TweetSentimentIndoBERT
IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding
https://github.com/asultan123/DL_LLC_Prefetching
https://github.com/athms/learning-from-brains
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/augustgw/early-exit-transformer
https://github.com/autobotasia/vibert
https://github.com/avichaychriqui/HeBERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/avishek-018/single_notebook_projects
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/aws-samples/amazon-sagemaker-bert-pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/aws-samples/aws-lex-noisy-spoken-language-understanding
https://github.com/awslabs/mlm-scoring
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/awslabs/sockeye
https://github.com/ays-dev/keras-transformer
https://github.com/azshue/TPT
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/babelscape/wikineural
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/bangoc123/transformer
https://github.com/baosongyang/WRD
https://github.com/baotramduong/Sentiment-Analysis-with-Deep-Learning-using-BERT
https://github.com/baotramduong/Twitter-Sentiment-Analysis-with-Deep-Learning-using-BERT
https://github.com/barisozmen/deepaugment
https://github.com/bartzi/chainer-transformer
https://github.com/basile-bron/Artificial-Intelligence-Lexical
https://github.com/baskargroup/Arboretum
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/baskargroup/biotrove
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/bcaitech1/p2-klue-Heeseok-Jeong
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/bcmi220/esc4nmt
https://github.com/bdusell/stack-attention
https://github.com/benjamintaiwo/Attention
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.