https://github.com/2023-MindSpore-4/Code4/tree/main/FBPrompt-main
Enhancing In-Context Learning with Answer Feedback for Multi-Span Question Answering
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/2023-MindSpore-4/Code6/tree/main/SDNet
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
https://github.com/2023-MindSpore-4/Code8/tree/main/AttentionCluster
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/audio_spectrogram_transformer
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/bart
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/bert_generation
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/big_bird
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/biogpt
BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/byt5
ByT5: Towards a token-free future with pre-trained byte-to-byte models
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/deberta
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/deberta_v2
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/distilbert
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/dpr
Fine-tune the Entire RAG Architecture (including DPR retriever) for Question-Answering
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/electra
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/ernie
ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/ernie_m
ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/2024-MindSpore-1/Code3/tree/main/gMLP
https://github.com/2024-MindSpore-1/Code3/tree/main/scBERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2miatran/Natural-Language-Processing
https://github.com/3D-ResNets-Reproduction/Reference-Project
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
https://github.com/3d-vista/3D-VisTA
3D-VisTA: Pre-trained Transformer for 3D Vision and Text Alignment
https://github.com/404nofound/gat
https://github.com/42Shawn/LLaVA-PruMerge
LLaVA-PruMerge: Adaptive Token Reduction for Efficient Large Multimodal Models
https://github.com/4pygmalion/GAT_tensorflow
https://github.com/8023looker/med-rr
PubMedQA: A Dataset for Biomedical Research Question Answering
https://github.com/ABaldrati/MT-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/AGiannoutsos/COVID19-document-retrieval-with-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/AI-metrics/AI-metrics
https://github.com/AI-team-UoA/GeoQuestions1089
https://github.com/AI4Bharat/indic-bert
https://github.com/AIPHES/acl19-generalization-capsule
Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications
https://github.com/AIResearchHub/transformergallery
https://github.com/AKSW/NLIWOD
https://github.com/AKSW/NSpM
Exploring Sequence-to-Sequence Models for SPARQL Pattern Composition
https://github.com/AUCOHL/RTL-Repo
https://github.com/AbhishekD15/Sentiment-Analysis_BERT
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.