https://github.com/AdamStein97/Semi-Supervised-BERT-NER
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Papers with Code 아카이브
Question Answering
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Question answering can be segmented into domain-specific tasks like community question answering and knowledge-base question answering. Popular benchmark datasets for evaluation question answering systems include SQuAD, HotPotQA, bAbI, TriviaQA, WikiQA, and many others. Models for question answering are typically evaluated on metrics like EM and F1. Some recent top performing models are T5 and XLNet.
( Image credit: SQuAD )
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Adapter-Hub/adapter-transformers
MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer
https://github.com/AhmedYounes94/Semi-supervised-BERT-NER
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Ahmmed44/question_answering_task
Intent Classification in Question-Answering Using LSTM Architectures
https://github.com/AishwaryaAgrawal/GVQA
Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering
https://github.com/AlanDongMu/TimeGAN_PytorchRebuild
https://github.com/AlanFokCo/compensation-tools
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/AleDella/mdter_eval
MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding
https://github.com/AlexKuhnle/ShapeWorld
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
https://github.com/AlexKuhnle/film
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
https://github.com/Alihassan7726/Fine-tuning-BERT-for-semantic-sentence-pairs-classification
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Alihassan7726/Multi-Class-classification-using-BERT-
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/AmenRa/rank_eval
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/Amy-1114/transformer
https://github.com/AnReu/T5-lenses
DecoderLens: Layerwise Interpretation of Encoder-Decoder Transformers
https://github.com/Anak2016/GAT
https://github.com/AndreaCossu/Relation-Network-PyTorch
https://github.com/AndreasInk/Quiz-APIv2
https://github.com/AndreiMoraru123/Neural-Machine-Translation
https://github.com/AndresPMD/Fine_Grained_Clf
https://github.com/Andrey885/Machine_translation_PyTorch
https://github.com/AngusMonroe/GAT-pytorch
https://github.com/Ankur3107/awesome-daily-blog
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/Ankur3107/dpr-tf
https://github.com/AnneDirkson/FuzzyBIO
https://github.com/Anou9531/GAT
https://github.com/AntheaLi/cs224nProject
End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems
https://github.com/Antonildo43/Classifica-o-de-textos-com-doc2Vec
https://github.com/ArdalanM/nlp-benchmarks
https://github.com/Arthurizijar/Bert_Airport
https://github.com/ArvidWartenberg/retrosynthesis
https://github.com/ArvinZhuang/BiTAG
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/AshwinDeshpande96/Hierarchical-Softmax
https://github.com/Aspire-Mayank/Top-Deep-Learning-Stuffs
https://github.com/AssafSinger94/sigmorphon-2020-inflection
https://github.com/Asteur/someChatbot
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.