https://github.com/DimasDMM/transformers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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/Doffery/BERT-Sentiment-Analysis-Amazon-Review
https://github.com/Domanjiri/joint-bert-with-tf2
https://github.com/Domminique/Deploy-BERT-for-Sentiment-Analysis-with-FastAPI-
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Dongcf/Pytorch_Bert_Text_Classification
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/DylanCope/Image-Captioning-with-Bimodal-Transformers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Eeun-ju/NLP-study
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ElizaLo/Question-Answering-based-on-SQuAD
https://github.com/EmilioTyl/transformers_from_scratch
https://github.com/Enigmatisms/Set-Transformer
https://github.com/Erutan-pku/LCN-for-Chinese-QA
Lattice-BERT: Leveraging Multi-Granularity Representations in Chinese Pre-trained Language Models
https://github.com/EssayKillerBrain/NLP-BERT-Chinese
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/EthanWYB/bert-classification
https://github.com/EugeneSel/EUMT
https://github.com/EuphoriaYan/bert_component
https://github.com/Farahn/AES
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/FaskyCC/TextClassification
https://github.com/FengJiaChunFromSYSU/Bert
https://github.com/Fireblossom/DeepDarkHomeword
Joint Slot Filling and Intent Detection via Capsule Neural Networks
https://github.com/Fireblossom/DeepDarkHomework
Joint Slot Filling and Intent Detection via Capsule Neural Networks
https://github.com/FlorianPfisterer/2D-LSTM-Seq2Seq
https://github.com/FreddeFrallan/Multilingual-CLIP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/Fuchai/language
https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction
https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction-roBERTa-5fold
https://github.com/Gahyeonkim09/AAPL
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/Gaozhen0816/BERT_QA_For_AILaw
https://github.com/Gaozhen0816/BERT_QA_for_Chinese
https://github.com/GauthierDmn/question_answering
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/GiuliaLanzillotta/INAS
https://github.com/Glaceon31/Document-Transformer
https://github.com/GlassyWing/transformer-keras
https://github.com/Guillem96/data2vec-vision
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
https://github.com/Guowei-Yan/Data
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/GustikS/NeuraLogic
https://github.com/HA-Transformer/HA-Transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.