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/mindspore-ai/contrib/tree/master/application/WinoGrande
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
https://github.com/mindspore-ai/models/blob/master/research/nlp/gpt2
https://github.com/mindspore-ai/models/tree/master/official/gnn/gat
https://github.com/mindspore-ai/models/tree/master/official/nlp/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/mindspore-ai/models/tree/master/official/nlp/bert_thor
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/mindspore-ai/models/tree/master/official/nlp/gpt
https://github.com/mindspore-ai/models/tree/master/official/nlp/pangu_alpha
https://github.com/mindspore-ai/models/tree/master/official/nlp/tinybert
TinyBERT: Distilling BERT for Natural Language Understanding
https://github.com/mindspore-ai/models/tree/master/official/nlp/transformer
https://github.com/mindspore-ai/models/tree/master/research/cv/resnet3d
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
https://github.com/mindspore-ai/models/tree/master/research/nlp/luke
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
https://github.com/mindspore-courses/ABSA-MindSpore
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/mindspore-courses/Deep-Tutorials-for-MindSpore
https://github.com/mindspore-courses/DeepNLP-models-MindSpore
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/attention/SelfAttention.py
https://github.com/mindspore-courses/d2l-mindspore/tree/master/chapter_14_natural_language_processing_pretraining
SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
https://github.com/mindspore-courses/d2l-mindspore/tree/master/chapter_15_natural_language_processing_applications
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/mindspore-lab/mindnlp/blob/master/mindnlp/models/albert/albert.py
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/mindspore-lab/mindnlp/blob/master/mindnlp/models/bert/bert.py
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/mineshmathew/DocVQA
https://github.com/mineshmathew/DocVQA/tree/master/BERT_baseline
https://github.com/ming024/FastSpeech2
https://github.com/mingdachen/bilm-tf
https://github.com/minimaxir/gpt-2-simple
https://github.com/minstar/BIDAF
https://github.com/miohana/vqa
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering
https://github.com/mithunpaul08/bert_tensorflow
https://github.com/mitran27/Attention-is-all-you-Need
https://github.com/mitvis/vistext
https://github.com/mitya8128/experiments_notes
https://github.com/mivg/sled
https://github.com/mkavim/finetune_bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/mlfoundations/open_flamingo
https://github.com/mlzxzhou/keras-gnm
https://github.com/mmSir/GainedVAE
Asymmetric Gained Deep Image Compression With Continuous Rate Adaptation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.