https://github.com/kyzhouhzau/BERT-NER
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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/labmlai/annotated_deep_learning_paper_implementations
https://github.com/laion-ai/open-assistant
Training language models to follow instructions with human feedback
https://github.com/lambert-x/prolab
https://github.com/laraolmos/madrid-nlp-meetup
SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation
https://github.com/lars-research/red-gnn
https://github.com/lashoun/hanna-benchmark-asg
https://github.com/lastmile-ai/aiconfig
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
https://github.com/lattice-ai/GPT
https://github.com/lavender105/rsgpt
Vision-Language Models in Remote Sensing: Current Progress and Future Trends
https://github.com/lcs2-iiitd/emotion-flip-reasoning
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lduml/blog
Stochastic Answer Networks for Machine Reading Comprehension
https://github.com/leaderj1001/Bag-of-MLP
https://github.com/leaguilar/baldwin_veering
https://github.com/learnables/learn2learn
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML
https://github.com/leehyunjun-seoultech/seoultech
https://github.com/leejieun51/transformer
https://github.com/leffff/any-domain-pretrain-gnns
https://github.com/lehinevych/SAAN
https://github.com/lehoanganh298/BERT-Question-Answering
https://github.com/lemonhu/ner-bert-pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lenacabrera/gb_mnmt
https://github.com/lennonzurich/lalala
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/mlp_family
https://github.com/leozhoujf/DataSciComp
https://github.com/lersouza/cross-lingual-transfer
On the Cross-lingual Transferability of Monolingual Representations
https://github.com/lesterpjy/numeric-t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/levon003/wiki-ores-feedback
https://github.com/lflage/openfactscore
https://github.com/lhao499/language-quantized-autoencoders
Language Quantized AutoEncoders: Towards Unsupervised Text-Image Alignment
https://github.com/liamdugan/question_generation
https://github.com/liano3/RAG-fairness
https://github.com/libertatis/mrc-cbt
https://github.com/lichengunc/pretrain-vl-data
https://github.com/lidongliang666/cv_deep_learning
https://github.com/lienchibao1998/new
Learning Visual Question Answering by Bootstrapping Hard Attention
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.