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/demelin/nematode
https://github.com/dengyang17/dengyang17.github.io
https://github.com/dengyh16code/MQA_dataset
https://github.com/denisewong1/ASX300
Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs
https://github.com/dennybritz/chatbot-retrieval
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
https://github.com/deping-1/2019-false-news-detection-challenge
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/derronxu/sparsebert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/derylucio/Transformer
https://github.com/devJWSong/bert-crf-entity-recognition-pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/devhemza/BERTweet_sentiment_analysis
https://github.com/devshree07/BERT-on-SQuAD
Comparative Study of Machine Learning Models and BERT on SQuAD
https://github.com/dexter-gt-86/sinklora
LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
https://github.com/dfcf93/MSMARCO
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/dhiraa/tener
https://github.com/dhlee347/pytorchic-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/dhyeon/ingredient-vectors
https://github.com/diabhaque/Sixth-Sense
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/dialogtekgeek/AudioVisualSceneAwareDialog
End-to-End Audio Visual Scene-Aware Dialog using Multimodal Attention-Based Video Features
https://github.com/dig-team/hanna-benchmark-asg
https://github.com/dimakarp1996/LAMB-keras
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/dimlight13/MU_SC_for_VQA
Task-Oriented Multi-User Semantic Communications for VQA Task
https://github.com/dinhvietcuong1996/icme25-inova
LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models
https://github.com/diridiri/ZeroShotCapsule
https://github.com/dispink/xpt
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/distractor-generation/dg_survey
https://github.com/dl-nlp/dl-nlp.github.io
https://github.com/dlvuldet/primevul
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
https://github.com/dmeoli/neuro-sat
https://github.com/dmis-lab/bern
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
https://github.com/dmis-lab/covidsearch
Contextualized Sparse Representations for Real-Time Open-Domain Question Answering
https://github.com/dmlc/dgl/tree/master/examples/mxnet/gat
https://github.com/dmlc/dgl/tree/master/examples/pytorch/gat
https://github.com/dmlc/dgl/tree/master/examples/pytorch/rrn
https://github.com/dmlc/dgl/tree/master/examples/tensorflow/gat
https://github.com/dmlc/gluon-nlp
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.