https://github.com/ahmetustun/fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
Papers with Code 아카이브
Question Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 11
- 보존된 평가 기록
- 42
The goal of Question Generation is to generate a valid and fluent question according to a given passage and the target answer. Question Generation can be used in many scenarios, such as automatic tutoring systems, improving the performance of Question Answering models and enabling chatbots to lead a conversation.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/airc-keti/ke-t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/allenai/c4-documentation
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/allenai/dolma
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/amazon-science/chronos-forecasting
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/artitw/BERT_QA
https://github.com/asahi417/lmppl
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/awinml/qg-lta
Learning to Ask: Neural Question Generation for Reading Comprehension
https://github.com/badripatro/MDN-VQG
Multimodal Differential Network for Visual Question Generation
https://github.com/bayer-science-for-a-better-life/data2text-bioleaflets
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/bigscience-workshop/architecture-objective
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/biswa380/t5_multitask_api
https://github.com/bjascob/amrlib
Investigating Pretrained Language Models for Graph-to-Text Generation
https://github.com/bloomsburyai/question-generation
Machine Comprehension by Text-to-Text Neural Question Generation
https://github.com/brynnchernosky/seq2seq
Keeping Notes: Conditional Natural Language Generation with a Scratchpad Mechanism
https://github.com/bzylich/persona-chatbot
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/cccntu/ft5-demo
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/cccntu/ft5-demo-space
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/chingyaoc/vqg-tensorflow
https://github.com/cindyxinyiwang/fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/codecaution/evomoe
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/conceptofmind/LaMDA-pytorch
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/conceptofmind/lamda-rlhf-pytorch
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/d294270681/ProphetNet-paddle
ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training
https://github.com/dangwalp/qg
Learning to Ask: Neural Question Generation for Reading Comprehension
https://github.com/dannigt/fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/dannigt/kit-iwslt2023-multilingual
https://github.com/dawn0815/UniSA
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/dmis-lab/gener
https://github.com/dreamstorm25/Generating-Natural-Questions-from-an-Image
https://github.com/facebookresearch/atlas
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/facebookresearch/data2vec_vision
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/facebookresearch/fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/fairseq/fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/flagopen/flagscale
Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data
https://github.com/formiel/fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.