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/huggingface/node-question-answering
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/huggingface/pytorch-image-models
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/huggingface/tflite-android-transformers
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_squeezebert.py
SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
https://github.com/huggingface/transformers/tree/master/src/transformers/models/byt5
ByT5: Towards a token-free future with pre-trained byte-to-byte models
https://github.com/huggingface/xlnet
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/hughsalimbeni/bayesian_benchmarks
Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors
https://github.com/hugochan/Graph2Seq-for-KGQG
Toward Subgraph-Guided Knowledge Graph Question Generation with Graph Neural Networks
https://github.com/huiwy/reflection-on-trees
Tree of Thoughts: Deliberate Problem Solving with Large Language Models
https://github.com/huiyinglu/NLP-Deep-Learning-Chatbot
https://github.com/hwang1996/IRGAN-pytorch
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
https://github.com/hwanheelee1993/vilbertscore
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
https://github.com/hyuna915/squad
Conditioning LSTM Decoder and Bi-directional Attention Based Question Answering System
https://github.com/hyunwookl/testam
https://github.com/hyunwoongko/transformer
https://github.com/i2r-simmc/i2r-simmc-2020
https://github.com/i404788/s5-pytorch
Hyena Hierarchy: Towards Larger Convolutional Language Models
https://github.com/iCompass-ai/TunBERT
https://github.com/iYiYaHa/PyTorch-IRGAN
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
https://github.com/iammrhelo/pytorch-vqa-dan
Dual Attention Networks for Multimodal Reasoning and Matching
https://github.com/iamwinter/Attentive-Pooling-Networks
https://github.com/ibm-self-serve-assets/metagen-blended-rag
MetaGen Blended RAG: Higher Accuracy for Domain-Specific Q&A Without Fine-Tuning
https://github.com/ibm/graph_ensemble_learning
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/ibm/techqa
https://github.com/ibm/vira-intent-discovery
https://github.com/ibrahimsharaf/doc2vec
https://github.com/icanswim/cxr
https://github.com/icewing1996/bert_dep
https://github.com/ictnlp/DiverseNMT
https://github.com/ictnlp/NA-MNMT
https://github.com/ictnlp/PTE-NMT
https://github.com/ictnlp/TLAT-NMT
https://github.com/idiap/abroad-re
https://github.com/idiotprofessorchen/bert.github.io
https://github.com/iejMac/ScriptWriter
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.