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/feizc/Machine-Translation-Pytorch
https://github.com/felixSchober/ABSA-Transformer
https://github.com/feyzaakyurek/XLM-LwLL
https://github.com/fiabdu/Commonly-Interesting-Images
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/filipbasara0/simple-clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/fjiangai/mmapis
https://github.com/flxst/nerblackbox
https://github.com/flyliu2017/bert
https://github.com/flyliu2017/bert_modularized
https://github.com/fra-mari/The-Karl-Marx-Press-Review
https://github.com/franckalain/Tensorflow-Projects-ChatBot
https://github.com/frankcgq105/BERTCHEN
https://github.com/franknb/Self-attention-DCGAN
https://github.com/franknb/Text-Summarization
https://github.com/fredxlpy/cross_lingual_prompt_tuning
https://github.com/fshdnc/enfi-XLM
https://github.com/fstahlberg/tensor2tensor
https://github.com/fuchason/chatbot
https://github.com/fuqiang-git-hub/unilmv1-Paddle
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/g-eklund/bert_tutorial
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/gaasher/data2vec2.0_vision
https://github.com/gabimelo/portuguese_wsc
https://github.com/gair-nlp/abel
Solving Quantitative Reasoning Problems with Language Models
https://github.com/ganeshjawahar/interpret_bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/gaohuan2015/NLPTool
Multi-Task Deep Neural Networks for Natural Language Understanding
https://github.com/gatech-eic/s3-router
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
https://github.com/gavincaoyuji/NLP
https://github.com/gbcolborne/lang_id
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/gcunhase/embracebert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/geondopark/ckd
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/gggggeun/DeepLearning_Architecture
https://github.com/giagia96/DeepComedy
https://github.com/giantseaweed/decree
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/glassroom/heinsen_routing
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/glouppe/info8010-deep-learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.