https://github.com/google-research-datasets/MultiReQA
공식 코드MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models
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/google-research-datasets/QED
공식 코드QED: A Framework and Dataset for Explanations in Question Answering
https://github.com/google-research-datasets/answer-equivalence-dataset
공식 코드Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation
https://github.com/google-research-datasets/aquamuse
공식 코드AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization
https://github.com/google-research-datasets/attributed-qa
공식 코드Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models
https://github.com/google-research-datasets/conceptual-12m
공식 코드Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts
https://github.com/google-research-datasets/disfl-qa
공식 코드Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering
https://github.com/google-research-datasets/egotempo
공식 코드Omnia de EgoTempo: Benchmarking Temporal Understanding of Multi-Modal LLMs in Egocentric Videos
https://github.com/google-research-datasets/indic-gen-bench
공식 코드https://github.com/google-research-datasets/maverics
공식 코드https://github.com/google-research-datasets/maxm
공식 코드https://github.com/google-research-datasets/qameleon
공식 코드https://github.com/google-research-datasets/quotesum
공식 코드https://github.com/google-research-datasets/screen_annotation
공식 코드ScreenAI: A Vision-Language Model for UI and Infographics Understanding
https://github.com/google-research-datasets/screen_qa
공식 코드ScreenQA: Large-Scale Question-Answer Pairs over Mobile App Screenshots
https://github.com/google-research-datasets/tydiqa
공식 코드TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages
https://github.com/google-research/ALBERT
공식 코드ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/google-research/bert
공식 코드BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/google-research/big_vision
공식 코드https://github.com/google-research/bigbird
공식 코드https://github.com/google-research/byt5
공식 코드ByT5: Towards a token-free future with pre-trained byte-to-byte models
https://github.com/google-research/dialog-inpainting
공식 코드https://github.com/google-research/electra
공식 코드ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
https://github.com/google-research/flan
공식 코드https://github.com/google-research/google-research
공식 코드https://github.com/google-research/google-research/tree/master/cfq
공식 코드Measuring Compositional Generalization: A Comprehensive Method on Realistic Data
https://github.com/google-research/google-research/tree/master/etcmodel
공식 코드https://github.com/google-research/google-research/tree/master/smith
공식 코드https://github.com/google-research/language
공식 코드https://github.com/google-research/language/tree/master/language/bert_extraction
공식 코드Thieves on Sesame Street! Model Extraction of BERT-based APIs
https://github.com/google-research/language/tree/master/language/emql
공식 코드https://github.com/google-research/language/tree/master/language/mentionmemory
공식 코드Mention Memory: incorporating textual knowledge into Transformers through entity mention attention
https://github.com/google-research/language/tree/master/language/multivec
공식 코드Sparse, Dense, and Attentional Representations for Text Retrieval
https://github.com/google-research/language/tree/master/language/orqa
공식 코드Latent Retrieval for Weakly Supervised Open Domain Question Answering
https://github.com/google-research/language/tree/master/language/realm
공식 코드https://github.com/google-research/longt5
공식 코드LongT5: Efficient Text-To-Text Transformer for Long Sequences
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.