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/Hibb-bb/ALLM
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Hironsan/anago
https://github.com/Holldean/BERT-Pruning
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/HuihuiChyan/BJTUNLP_Practice2020
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/HuihuiChyan/BJTUNLP_Practice2021
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/HyeonwooNoh/DPPnet
Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction
https://github.com/HzcIrving/DeepLearning_PlayGround
https://github.com/IAAR-Shanghai/UHGEval
https://github.com/IBM/ColBERT-practical
https://github.com/IBM/Dromedary
https://github.com/IBM/MAX-Question-Answering
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/IBM/MAX-Text-Sentiment-Classifier
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/IBM/MAX-Toxic-Comment-Classifier
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/IBM/helm-enterprise-benchmark
https://github.com/IBM/pytorch-seq2seq
https://github.com/IDEA-FinAI/Simple-HHEA
Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph
https://github.com/IGITUGraz/H-Mem
https://github.com/IMPLabUniPr/UniParma-at-semeval-2021-task-5
https://github.com/INK-USC/CommonGen
CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning
https://github.com/IOB-Muenster/TEclass2
https://github.com/IllgamhoDuck/ResTR
https://github.com/IllinoisGraphBenchmark/IGB-Datasets
https://github.com/Impavidity/relogic
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ImperialNLP/BertGen
VL-BERT: Pre-training of Generic Visual-Linguistic Representations
https://github.com/IndicoDataSolutions/finetune
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/InsaneLife/ChineseNLPCorpus
https://github.com/InternLM/opencompass
BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack
https://github.com/IrishCoffee/cudnnMultiHeadAttention
https://github.com/Iwontbecreative/Abstractive-summarization-OpenNMT
https://github.com/Izecson/saml-nmt
https://github.com/Izecson/sockeye-1.16.6
https://github.com/JA-Bar/nlp-depression
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/JHart96/keras_elmo_embedding_layer
https://github.com/JMSaindon/NlpSquad
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/JNUpython/bert
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.