Papers with Code 아카이브
Question Answering
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- 하위 태스크
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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/mgmalek/efficient_cross_entropy
https://github.com/mhamedLmarbouh/Attention-is-all-you-need
https://github.com/mhardalov/customer-support-chatbot
https://github.com/mhaseeb123/DeepMSim
https://github.com/mia-workshop/mia-shared-task-2022
Latent Retrieval for Weakly Supervised Open Domain Question Answering
https://github.com/michalspiegel/imgtb
https://github.com/microcoder-py/attn-is-all-you-need
https://github.com/microsoft/BIPIA
https://github.com/microsoft/HittER
HittER: Hierarchical Transformers for Knowledge Graph Embeddings
https://github.com/microsoft/K-Adapter
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
https://github.com/microsoft/KC/tree/main/papers/ReTraCk
https://github.com/microsoft/LLaVA-Med
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day
https://github.com/microsoft/MSMARCO-Conversational-Search
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/MSMARCO-Document-Ranking
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/MSMARCO-OpenKP
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/MSMARCO-Passage-Ranking
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/MSMARCO-Question-Answering
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/OpenKP
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/TREC-2019-Deep-Learning
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/TUTA_table_understanding
Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks
https://github.com/microsoft/Table-Pretraining
https://github.com/microsoft/Transformer-XH
https://github.com/microsoft/automated-explanations
https://github.com/microsoft/fastseq
https://github.com/microsoft/fs-mol
https://github.com/microsoft/graphrag
From Local to Global: A Graph RAG Approach to Query-Focused Summarization
https://github.com/microsoft/guidance
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
https://github.com/microsoft/kear
Fusing Context Into Knowledge Graph for Commonsense Question Answering
https://github.com/microsoft/onnxruntime-training-examples
https://github.com/microsoft/promptbase
Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
https://github.com/midobal/OpenNMT-py
https://github.com/migalkin/NodePiece
https://github.com/migalkin/NodePiece/tree/main/ogb
https://github.com/milmor/GPT
https://github.com/mim-solutions/bert_for_longer_texts
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.