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/PaddlePaddle/PaddleNLP/blob/develop/paddlenlp/transformers/roberta/modeling.py
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/dialogue/plato-2
PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/few_shot/efl
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/end_to_end_memory_networks
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/gpt
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/gpt-3
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/megatronbert
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/rembert
Rethinking embedding coupling in pre-trained language models
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/roformer
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/xlnet
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/machine_translation/transformer
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/electra
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/paddlenlp/transformers/bigbird
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/paddlenlp/transformers/tinybert
TinyBERT: Distilling BERT for Natural Language Understanding
https://github.com/PaddlePaddle/PaddleSpeech
https://github.com/PaddlePaddle/models
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Pezhin/best_yelp_review_project_ever
https://github.com/PhysGame/PhysGame
PhysGame: Uncovering Physical Commonsense Violations in Gameplay Videos
https://github.com/PierreSenellart/theoremkb
Extracting Definienda in Mathematical Scholarly Articles with Transformers
https://github.com/Pirata-Codex/Tag-Persian-Entities-Using-Bert
https://github.com/Pointcept/GPT4Point
https://github.com/PrashantRanjan09/Elmo-Tutorial
https://github.com/PrashantRanjan09/WordEmbeddings-Elmo-Fasttext-Word2Vec
https://github.com/PrideLee/sentiment-analysis
https://github.com/PriyalNarang/Tapas-Experiments
https://github.com/Priyanshiguptaaa/CoLA_Sentence_Classification_BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ProsusAI/finBERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/PumpkinYing/GAT
https://github.com/Qordobacode/fitbert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/QwenLM/Qwen2.5-Omni
https://github.com/R1j1t/contextualSpellCheck
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/RAGHAVJHA01/chatboat
https://github.com/RUCAIBox/LLMBox
https://github.com/Raghuvar/pneumonia_detection_using_X-rays
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.