Papers with Code 아카이브
Question Answering
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- 평가표
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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/longhao-chen/aicas2024
https://github.com/lottens/QA-SQuAD
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
https://github.com/louis-udm/ner-bert-crf
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lovedavidsilva/bert_old_version
https://github.com/ltgoslo/assessing_and_probing_sentiment
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ltruncel/Microsoft_Azure_50daysofudacity
https://github.com/lucashueda/long_sentence_transformer
https://github.com/lucidrains/AoA-pytorch
https://github.com/lucidrains/CoCa-pytorch
https://github.com/lucidrains/FLASH-pytorch
https://github.com/lucidrains/PaLM-jax
https://github.com/lucidrains/PaLM-pytorch
https://github.com/lucidrains/RETRO-pytorch
Improving language models by retrieving from trillions of tokens
https://github.com/lucidrains/compressive-transformer-pytorch
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/lucidrains/electra-pytorch
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
https://github.com/lucidrains/flamingo-pytorch
https://github.com/lucidrains/g-mlp-gpt
https://github.com/lucidrains/g-mlp-pytorch
https://github.com/lucidrains/mammut-pytorch
MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks
https://github.com/lucidrains/marge-pytorch
Distilling Knowledge from Reader to Retriever for Question Answering
https://github.com/lucidrains/mlm-pytorch
https://github.com/lucidrains/mlp-gpt-jax
https://github.com/lucidrains/nystrom-attention
Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention
https://github.com/lucidrains/ponder-transformer
https://github.com/lucidrains/reformer-pytorch
https://github.com/lucidrains/routing-transformer
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/lucidrains/sinkhorn-transformer
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/lucidrains/x-transformers
https://github.com/luckynozomi/PPI_Bert
https://github.com/lucylow/En_francais_si_vous_plait-
https://github.com/luisrui/Modality-Interference-in-MLLMs
Diagnosing and Mitigating Modality Interference in Multimodal Large Language Models
https://github.com/lukecavabarrett/pna
https://github.com/lukemelas/PyTorch-Pretrained-ViT
https://github.com/lukexyz/Deep-Lyrical-Genius
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/luogen1996/lavin
Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.