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/danlim-wz/analysis_of_social_media_data_about_singapore
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/danny911kr/REALTALK
Evaluating Very Long-Term Conversational Memory of LLM Agents
https://github.com/dare0021/MemN2N_Bench
https://github.com/darr/nerbert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/darr/nlp_work
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/dashenzi721/hra
https://github.com/david-yoon/QA_HRDE_LTC
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
https://github.com/davidalbertonogueira/NLP-tutorials
https://github.com/davidbellamy/labrador
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/davidgolub/QuestionGeneration
https://github.com/davidgolub/SimpleQA
https://github.com/davidpicard/homm
https://github.com/davidsvaughn/dts-tf
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/davisking/dlib-models
https://github.com/davisliang/fairseq/tree/main/examples/xlmv
XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models
https://github.com/dawn0815/UniSA
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/dayyass/QaNER
QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognition
https://github.com/dbmdz/historic-ner
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/dccuchile/CC6204
https://github.com/debjitpaul/refiner
Are NLP Models really able to Solve Simple Math Word Problems?
https://github.com/debnsuma/AmazonSageMaker101
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/debnsuma/Intro-Transformer-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/decile-team/cords
Coresets for Data-efficient Training of Machine Learning Models
https://github.com/declare-lab/flan-alpaca
https://github.com/deep-spin/quati
https://github.com/deepmind/haiku
https://github.com/deepmind/mathematics_dataset
https://github.com/deepmind/narrativeqa
https://github.com/deepmind/tracr
https://github.com/deepmipt/DeepPavlov
LSTM-based Deep Learning Models for Non-factoid Answer Selection
https://github.com/deepmipt/Slavic-BERT-NER
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/deepmipt/bert/tree/feat/multi_gpu
https://github.com/deepopinion/anls-star-metric
Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answering
https://github.com/deepopinion/anls_star_metric
Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answering
https://github.com/deepset-ai/FARM
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.