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/c00k1ez/plain-transformers
Reducing Transformer Depth on Demand with Structured Dropout
https://github.com/cactilab/hateguard
https://github.com/cadia-lvl/fastspeech2
https://github.com/cahya-wirawan/indonesian-language-models
https://github.com/caihuaiguang/CHG-Shapley-for-Data-Selection
Coresets for Data-efficient Training of Machine Learning Models
https://github.com/cair/PyTsetlinMachineCUDA
A Relational Tsetlin Machine with Applications to Natural Language Understanding
https://github.com/cair/TsetlinMachine
A Relational Tsetlin Machine with Applications to Natural Language Understanding
https://github.com/cair/pyTsetlinMachine
A Relational Tsetlin Machine with Applications to Natural Language Understanding
https://github.com/cair/pyTsetlinMachineMT
A Relational Tsetlin Machine with Applications to Natural Language Understanding
https://github.com/cair/pyTsetlinMachineParallel
A Relational Tsetlin Machine with Applications to Natural Language Understanding
https://github.com/calciver/Graph-Attention-Networks
https://github.com/camlaedtke/NLP
https://github.com/candoz/memn2n-dialog-babi-keras
https://github.com/canlinzhang/siailp
https://github.com/caoyujiALgLM/NLP
https://github.com/caoyujiALgLM/NLP-progress
https://github.com/carpedm20/variational-text-tensorflow
https://github.com/carrliitos/NLPInformationExtraction
https://github.com/castorini/Anserini
https://github.com/castorini/birch
https://github.com/castorini/docTTTTTquery
https://github.com/castorini/mr.tydi
TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages
https://github.com/castorini/nuggetizer
Initial Nugget Evaluation Results for the TREC 2024 RAG Track with the AutoNuggetizer Framework
https://github.com/castorini/pygaggle
Rapidly Bootstrapping a Question Answering Dataset for COVID-19
https://github.com/cccntu/ft5-demo
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/cccntu/ft5-demo-space
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/cdhx/queryagent
https://github.com/cdmdc/lr-identify-custom
https://github.com/cedrickchee/pytorch-pretrained-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/chail/latent-composition
Using latent space regression to analyze and leverage compositionality in GANs
https://github.com/chaitanyadwivedii/3D-Attention-is-All-You-Need
TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering
https://github.com/chakravarthi-v/Polaroid-1
https://github.com/chalothon/BERT_Practice
https://github.com/chalothon/Cnn-Daily-mail
https://github.com/chambliss/Multilingual_NER
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.