https://github.com/elenalenaelena/machine-learning-viz
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Papers with Code 아카이브
Question Answering
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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/emanuele-progr/PSSP
https://github.com/emorynlp/friendsqa
Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering
https://github.com/emrgnt-cmplxty/zero-shot-replication
https://github.com/enhuiz/torchnmt
https://github.com/enhuiz/transformer-pytorch
https://github.com/enod/arxiv-nlp-notes
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/enzomuschik/distilfnd
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/epcpu/deep-nlp-links
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/epfl-dlab/multilingual-entity-insertion
https://github.com/epfml/collaborative-attention
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/eric-erki/decaNLP
The Natural Language Decathlon: Multitask Learning as Question Answering
https://github.com/eric-mitchell/mend
https://github.com/eric-mitchell/serac
https://github.com/ericchen321/ros_x_habitat
https://github.com/ericperfect/Bert_Position_BiLSTM_Attention_CRF_LSTMDecoder
https://github.com/eske/multivec
https://github.com/espectre/GCNs_on_imbalanced_datasets
https://github.com/etendue/habitat-api
https://github.com/eternityyw/tram-benchmark
https://github.com/eth-easl/deltazip
SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot
https://github.com/ethancaballero/Improved-Dynamic-Memory-Networks-DMN-plus
Dynamic Memory Networks for Visual and Textual Question Answering
https://github.com/ethancaballero/neural-engineers-first-attempt
https://github.com/ethanjperez/film
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
https://github.com/ethanjperez/rda
https://github.com/ethanjperez/true_few_shot
https://github.com/ethz-privsec/superhuman-ai-consistency
https://github.com/ethz-spylab/superhuman-ai-consistency
https://github.com/eunseo1092/Graduation_project
https://github.com/evtaktasheva/dependency_extraction
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ewdowiak/Sicilian_Translator
https://github.com/ewrfcas/QANet_keras
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
https://github.com/ewrfcas/Reinforced-Mnemonic-Reader
Reinforced Mnemonic Reader for Machine Reading Comprehension
https://github.com/exa-labs/exa-mcp-server
https://github.com/expertailab/spaceqa
https://github.com/expz/annotated-hyena
Hyena Hierarchy: Towards Larger Convolutional Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.