Papers with Code 아카이브
Question Answering
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- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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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/jiyuan/ainote
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/jlrussin/interpret-math-transformer
https://github.com/jm12138/Paddle-CheXNet
https://github.com/jmnolte/hccnet
https://github.com/joaopedromattos/gnee
https://github.com/joe-prog/https-github.com-facebookresearch-ParlAI
https://github.com/joebluems/Soul_Of_AI
https://github.com/joel-huang/zeroshot-capsnet-pytorch
https://github.com/joeljang/rlphf
Exploring the Benefits of Training Expert Language Models over Instruction Tuning
https://github.com/joelniklaus/lawinstruct
https://github.com/john-bradshaw/rxn-lm
https://github.com/johnr0/TaleBrush-backend
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/johntiger1/multitask_multimodal
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
https://github.com/jojonki/MemoryNetworks
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
https://github.com/jojonki/key-value-memory-networks
https://github.com/jonahanton/ssl_audio
https://github.com/jonahwinninghoff/Text-Summarization
https://github.com/jongwooko/nash-pruning-official
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/joongbo/tta
https://github.com/jopokemine/Dissertation
https://github.com/joshuaWang-bit/Textclassification-pytorch
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/joyhsu0504/ns3d
https://github.com/joytianya/google_bert
https://github.com/jozhang97/side-tuning
Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks
https://github.com/jpablou/Matching-The-Blanks-Ths
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
https://github.com/jpthu17/dicosa
Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning
https://github.com/jpthu17/diffusionret
Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning
https://github.com/jpthu17/emcl
Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning
https://github.com/jrzech/reproduce-chexnet
https://github.com/jsantoso2/yelp-clone-ml-project
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/jules-samaran/vl-bert
VL-BERT: Pre-training of Generic Visual-Linguistic Representations
https://github.com/juletx/lm-evaluation-harness
https://github.com/julian-risch/ICADL2018
https://github.com/julianser/Ubuntu-Multiresolution-Tools
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
https://github.com/julianser/hed-dlg-truncated
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.