Papers with Code 아카이브
Question Answering
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- 평가표
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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/DevSinghSachan/art
End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering
https://github.com/DevSinghSachan/multilingual_nmt
https://github.com/Diego999/pyGAT
https://github.com/DigitalBiomarkerDiscoveryPipeline/Human-Activity-Recognition
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing
https://github.com/DimasDMM/nlp-completer
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/DimasDMM/transformers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Divs1159/STING-BEE
STING-BEE: Towards Vision-Language Model for Real-World X-ray Baggage Security Inspection
https://github.com/Doffery/BERT-Sentiment-Analysis-Amazon-Review
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/DongjunLee/dmn-tensorflow
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
https://github.com/DylanCope/Image-Captioning-with-Bimodal-Transformers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/EMBEDDIA/event-detection
Event Detection as Question Answering with Entity Information
https://github.com/EMalagoli92/PonderNet-TensorFlow
https://github.com/Eeun-ju/NLP-study
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/EightRice/atn_GPT-3
https://github.com/EleutherAI/gpt-neo
https://github.com/EleutherAI/lm-evaluation-harness
https://github.com/EleutherAI/lm_evaluation_harness
https://github.com/ElizaLo/Question-Answering-based-on-SQuAD
https://github.com/Elvirasun28/quora-question-duplicate
Convolutional Neural Network Architectures for Matching Natural Language Sentences
https://github.com/EmanueleC/DRMM_repro
https://github.com/EmilioTyl/transformers_from_scratch
https://github.com/EmilyAlsentzer/clinicalBERT
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
https://github.com/EmptyJackson/groove
https://github.com/Enigmatisms/Set-Transformer
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/Fangyin1994/KCL
https://github.com/Farahn/AES
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/FaskyCC/TextClassification
https://github.com/FenQQQ/Fusion-in-decoder
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
https://github.com/FengJiaChunFromSYSU/Bert
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.