https://github.com/CMSC470-Team/Model
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
Papers with Code 아카이브
Question Answering
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- 평가표
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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/COMP6248-Reproducability-Challenge/Differentiables_FastAccurateTextClassification
https://github.com/CVxTz/music_genre_classification
https://github.com/CZWin32768/xnlg
MT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs
https://github.com/Caesarzhang/bert-zh
https://github.com/CalumPerrio/WNUT-2020
https://github.com/Cardal/Kaggle_AllenAIscience
Moving Beyond the Turing Test with the Allen AI Science Challenge
https://github.com/CatherineWong/dancin_seq2seq
TextBugger: Generating Adversarial Text Against Real-world Applications
https://github.com/Chang-Chia-Chi/SaintPlus-Knowledge-Tracing-Pytorch
https://github.com/Charliesgithub20221030/BERT
https://github.com/Chat-3D/Chat-3D
Chat-Scene: Bridging 3D Scene and Large Language Models with Object Identifiers
https://github.com/ChengSashankh/trying-GAT
https://github.com/ChengyuSun/gat
https://github.com/Chenny0808/ape210k
Ape210K: A Large-Scale and Template-Rich Dataset of Math Word Problems
https://github.com/ChernovAndrey/chronos-forecasting-wasserstein
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/China-UK-ZSL/Resources_for_KZSL
https://github.com/Chinnu1103/Machine-Translation-using-Transformers
https://github.com/Chonwai/Learning_BERT
https://github.com/ClaudiaRaffaelli/Protein-subcellular-localization
https://github.com/Cloud-CV/vilbert-multi-task
12-in-1: Multi-Task Vision and Language Representation Learning
https://github.com/Coda-s/BJTU_NLP_Practice
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/CodingWitcher/Chexnet_NIH
https://github.com/Colanim/BERT_STS-B
https://github.com/ColdenChan/GAT_D
https://github.com/Conchylicultor/DeepQA
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
https://github.com/CyberZHG/keras-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/DCYN/Ramdomized-Clinical-Trail-Classification
https://github.com/DJJune/Transformer
https://github.com/DanieleVeri/deep_comedy
https://github.com/DataScienceNigeria/ALBERT-for-Natural-Language-Processing
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/DataScienceNigeria/ERNIE-2.0-from-Baidu-Inc.
ERNIE 2.0: A Continual Pre-training Framework for Language Understanding
https://github.com/DataScienceNigeria/Supervised-NLP-from-Facebook-AI-Team
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Dave870907/Transformer
https://github.com/Deepak-Work/Passage_Retrieval
https://github.com/DeligientSloth/QQsim
https://github.com/DeligientSloth/bert-tensorflow
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.