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/priya22/emotion-granularity-from-text
https://github.com/priya22/emotiondynamics
https://github.com/priyamtejaswin/multistep-retrieve-summarize
https://github.com/projectdossier/medvid2022
A Dataset for Medical Instructional Video Classification and Question Answering
https://github.com/ps2811/Bert-Multi-Label-Text-Classification-GitVersion
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ptillet/torch-blocksparse
https://github.com/pwc-1/Paper-10/tree/main/squeezebert
SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
https://github.com/pwc-1/Paper-10/tree/main/switch_transformers
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
https://github.com/pwc-1/Paper-10/tree/main/tapas
https://github.com/pwc-1/Paper-10/tree/main/tapex
Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks
https://github.com/pwc-1/Paper-10/tree/main/time_series_transformer
https://github.com/pwc-1/Paper-10/tree/main/tinybert
TinyBERT: Distilling BERT for Natural Language Understanding
https://github.com/pwc-1/Paper-5/tree/main/mluke
mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models
https://github.com/pwc-1/Paper-5/tree/main/mobilebert
MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
https://github.com/pwc-1/Paper-5/tree/main/mt5
mT5: A massively multilingual pre-trained text-to-text transformer
https://github.com/pwc-1/Paper-8/tree/main/albert
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/pwc-1/Paper-8/tree/main/big_bird
https://github.com/pwc-1/Paper-8/tree/main/bigbird_pegasus
https://github.com/pwc-1/Paper-8/tree/main/deberta
https://github.com/pwc-1/Paper-8/tree/main/deberta_v2
https://github.com/pwc-1/Paper-9/tree/main/1/tinybert
TinyBERT: Distilling BERT for Natural Language Understanding
https://github.com/pwc-1/Paper-9/tree/main/1/xlnet
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/pwc-1/Paper-9/tree/main/2/minigpt4
https://github.com/pwc-1/Paper-9/tree/main/2/mistral
https://github.com/pwc-1/Paper-9/tree/main/2/mixtral
https://github.com/pwc-1/Paper-9/tree/main/2/mluke
mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models
https://github.com/pwc-1/Paper-9/tree/main/2/mt5
MT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs
https://github.com/pwc-1/Paper-9/tree/main/2/nystromformer
Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention
https://github.com/pwc-1/Paper-9/tree/main/3/WinoGrande
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
https://github.com/pwc-1/Paper-9/tree/main/3/stable-sam
Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound
https://github.com/pwc-1/Paper-9/tree/main/4/Translation-Invariant/model
https://github.com/pwc-1/Paper-9/tree/main/4/WinoGrande
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
https://github.com/pwc-1/Paper-9/tree/main/4/stable-sam
Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound
https://github.com/pwc-1/Paper-9/tree/main/5/xlm
XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models
https://github.com/pwc-1/Paper-9/tree/main/5/xlm_roberta
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.