Papers with Code 아카이브
Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 19
- 평가표
- 153
- 보존된 평가 기록
- 1,781
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/MindCode-4/code-3/tree/main/ernie
ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/MindCode-4/code-3/tree/main/ernie_m
ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/MindCode-4/code-4/tree/main/qwen2_moe
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
https://github.com/MindCode-4/code-4/tree/main/qwen2_vl
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
https://github.com/MindCode-4/code-4/tree/main/seamless_m4t
https://github.com/MindCode-4/code-5/tree/main/tapas
https://github.com/MindCode-4/code-5/tree/main/time_series_transformer
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
https://github.com/MindCode-4/code-5/tree/main/tinybert
TinyBERT: Distilling BERT for Natural Language Understanding
https://github.com/MindCode-4/code-5/tree/main/transformer
https://github.com/MindCode-4/code-5/tree/main/tvlt
https://github.com/MindCode-4/code-5/tree/main/vision_text_dual_encoder
Distilled Dual-Encoder Model for Vision-Language Understanding
https://github.com/MindCode-4/code-5/tree/main/xlnet
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/MindCode-4/code-6/tree/main/a-self-attentive-model-for-knowledge-tracing
https://github.com/MindCode-4/code-6/tree/main/drop-an-octave-reducing-spatial
https://github.com/MindCode-4/code-7/tree/main/Large-Batch-Optimization-for-Deep-Learning
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/MindCode-4/code-8/tree/main/perceptual-score
Perceptual Score: What Data Modalities Does Your Model Perceive?
https://github.com/MindCode-4/code-9/tree/main/stable-sam
Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound
https://github.com/MindSpore-paper-code-2/code399/tree/main/Bert
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/MindSpore-paper-code-2/code400/tree/main/MAE
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training
https://github.com/MindSpore-paper-code-3/code10/tree/main/guided_anchoring
https://github.com/MindSpore-paper-code-3/code9/tree/main/albert
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/MindSpore-paper-code-3/code9/tree/main/emotect
ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/MindSpore-paper-code-3/code9/tree/main/soft_masked_bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/MindSpore-paper-code-3/code9/tree/main/textrcnn
Deep Learning Based Text Classification: A Comprehensive Review
https://github.com/MindSpore-scientific-2/code-1/tree/main/LIE-IQA
IQA: Interactive Query Construction in Semantic Question Answering Systems
https://github.com/MindSpore-scientific-2/code-10/tree/main/MicAugment
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/MindSpore-scientific-2/code-10/tree/main/Quiz_gen
Can an AI Win Ghana's National Science and Maths Quiz? An AI Grand Challenge for Education
https://github.com/MindSpore-scientific-2/code-10/tree/main/nystrom-attention
Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention
https://github.com/MindSpore-scientific-2/code-12/tree/main/Large-Batch-Optimization-for-Deep-Learning
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/MindSpore-scientific-2/code-12/tree/main/gaussian_adaptive_attention
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/MindSpore-scientific-2/code-12/tree/main/isr
ISR-DPO: Aligning Large Multimodal Models for Videos by Iterative Self-Retrospective DPO
https://github.com/MindSpore-scientific-2/code-14/tree/main/layoutlmv3
LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
https://github.com/MindSpore-scientific-2/code-14/tree/main/llama
https://github.com/MindSpore-scientific-2/code-14/tree/main/llava_next
https://github.com/MindSpore-scientific-2/code-14/tree/main/longt5
LongT5: Efficient Text-To-Text Transformer for Long Sequences
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.