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/wayalhruhi/julianser
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
https://github.com/wayveai/driving-with-llms
https://github.com/wchh127/yykf
https://github.com/weidafeng/NLU2019
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/weixin-liang/metashift
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering
https://github.com/weixsong/bilm-tf
https://github.com/weiyangfb/PyTorchSparseGAT
https://github.com/wenet-e2e/west
https://github.com/wenhuchen/HDSA-Dialog
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/wenhuchen/gnn-tabfact
NumNet: Machine Reading Comprehension with Numerical Reasoning
https://github.com/white127/squad-2.0-bidaf
https://github.com/whqwill/seq2seq-keyphrase-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/whr94621/NJUNMT-pytorch
https://github.com/whut2962575697/gat_sementic_segmentation
https://github.com/why2000/DuReader-bert
https://github.com/wi-pi/GDPR
Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep Learning
https://github.com/wiflore/IBM_Articles_Recomender
https://github.com/wikipedia2vec/wikipedia2vec/tree/master/examples/text_classification
Neural Attentive Bag-of-Entities Model for Text Classification
https://github.com/willemsenbram/mention-detection-vgd
https://github.com/wilson1yan/VideoGPT
https://github.com/winfredge/t2s
Position: What Can Large Language Models Tell Us about Time Series Analysis
https://github.com/winter1997/Attentive-Pooling-Networks
https://github.com/woodcutter1998/ogb-grail-mod
https://github.com/wooseok-AI/Korean_e2e_CR_BERT
SpanBERT: Improving Pre-training by Representing and Predicting Spans
https://github.com/writerai/fitbert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/wszcaswsz/Conversation_Model
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
https://github.com/wujsAct/DeepLearningModels
https://github.com/wusize/harmon
https://github.com/wuyuejinxia/prcv2019-mvb-renet
MVB: A Large-Scale Dataset for Baggage Re-Identification and Merged Siamese Networks
https://github.com/wxh1996/VideoAgent
VideoAgent: Long-form Video Understanding with Large Language Model as Agent
https://github.com/wxt1997/Transformer-Transducer
https://github.com/wyf-python/Bert-Pytorch-TextClassification
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/wyu97/Easy-use-BART
https://github.com/wzlxjtu/PositionalEncoding2D
https://github.com/wzzzd/LM_NER
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.