Papers with Code 아카이브
Natural Language Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 6
- 보존된 평가 기록
- 42
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/idiotprofessorchen/bert.github.io
https://github.com/iejMac/ScriptWriter
https://github.com/iflytek/cino
https://github.com/iglu-contest/nlp-baselines-2022
https://github.com/iit-nlp-research/chatgpt-crawler
https://github.com/ikergarcia1996/Simultaneous-Machine-Translation
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/imran3180/pytorch-nli
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
https://github.com/innerNULL/PLM-ICD-multi-label-classifier
PLM-ICD: Automatic ICD Coding with Pretrained Language Models
https://github.com/insigh/THUMT
https://github.com/ishritam/Image-captioning-with-visual-attention
https://github.com/istar1978/plato
Collaborative Multi-Agent Dialogue Model Training Via Reinforcement Learning
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/jackaduma/ChatGLM-LoRA-RLHF-PyTorch
GLM: General Language Model Pretraining with Autoregressive Blank Infilling
https://github.com/jadore801120/attention-is-all-you-need-pytorch
https://github.com/jageshmaharjan/BERT_Service
https://github.com/jaisenbe58r/NLP-Transformer_Translator
https://github.com/jaketae/tupe
https://github.com/jamesdvance/predicting_clicks
https://github.com/jangjoongkeon/JK
https://github.com/jbdel/OMG_UMONS_submission
https://github.com/jbeno/sentiment
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
https://github.com/jean-kunz/ml_research_papers
https://github.com/jelifysh/Transformers
https://github.com/jellyfish042/rwkv-statetuning
https://github.com/jeongchanyoung-1234/De-En-NMT
https://github.com/jeongukjae/KR-BERT-SimCSE
KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding
https://github.com/jgcbrouns/Introducing-Sparsity-in-the-Transformer
https://github.com/jhaprince/multibully
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/jhgan00/image-retrieval-transformers
https://github.com/jiachangliu/kategpt3
https://github.com/jiahuei/Self-Critical-SCST-TensorFlow
https://github.com/jiaruncao/BioCopyMechanism
Unified Language Model Pre-training for Natural Language Understanding and Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.