https://github.com/thudm/swissarmytransformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Papers with Code 아카이브
Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 19
- 평가표
- 153
- 보존된 평가 기록
- 1,781
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/thumt/THUMT
https://github.com/thunlp-mt/ckd
https://github.com/thunlp-mt/promptgating4mctg
Reducing Transformer Depth on Demand with Structured Dropout
https://github.com/thunlp/OpenQA
https://github.com/thunlp/ReInfoSelect
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/thunlp/clueanchor
Atlas: Few-shot Learning with Retrieval Augmented Language Models
https://github.com/thunlp/pevl
MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding
https://github.com/tighu20/Kaggle-Tweet-Sentiment-Extraction
https://github.com/tikyau/bidaf-quick-test
https://github.com/timrozday/spl-indications-bart
https://github.com/tizzzzy/demonstration_selection_overview
https://github.com/tjrwlgns1198/ChatBot
https://github.com/tkkiran/DIAG
Stochastic subgradient method converges at the rate $O(k^{-1/4})$ on weakly convex functions
https://github.com/tlmakinen/GAT-noise
https://github.com/tmalbonph/tcbert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/tobias-opsahl/fact-or-fiction
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
https://github.com/tobyatgithub/bert_tutorial
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/togethercomputer/llama-2-7b-32k-instruct
https://github.com/togethercomputer/stripedhyena
Hyena Hierarchy: Towards Larger Convolutional Language Models
https://github.com/tomdyer10/fake_news
https://github.com/tomerwolgithub/Break
https://github.com/tomgoter/nlp_finalproject
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/tongji-kgllm/rag-survey
Retrieval-Generation Synergy Augmented Large Language Models
https://github.com/tongjingqi/code2logic
https://github.com/tonyzhaozh/few-shot-learning
https://github.com/tornermarton/bert-keras
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/totalgood/viddesc
https://github.com/toumix/discopy
https://github.com/traviscoan/cards
https://github.com/tree-park/bert-pretrainer
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/tree-park/kor-to-eng-translation
https://github.com/tree-park/transformer_lm
https://github.com/trisha-c-a/Visual-Question-Answering-with-Keras
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding
https://github.com/trisongz/question-answering-lightning
https://github.com/truclycs/ocr_seq2seq
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.