https://github.com/azshue/TPT
Learning Transferable Visual Models From Natural Language Supervision
Papers with Code 아카이브
Text Generation
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- 평가표
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- 보존된 평가 기록
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Text Generation is the task of generating text with the goal of appearing indistinguishable to human-written text. This task is more formally known as "natural language generation" in the literature.
Text generation can be addressed with Markov processes or deep generative models like LSTMs. Recently, some of the most advanced methods for text generation include BART, GPT and other GAN-based approaches. Text generation systems are evaluated either through human ratings or automatic evaluation metrics like METEOR, ROUGE, and BLEU.
Further readings:
<span style="color:grey; opacity: 0.6">( Image credit: Adversarial Ranking for Language Generation )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/b-etienne/Seq2seq-PyTorch
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/baaivision/eva
https://github.com/baber-sos/Explaining-Dialogue-Evaluation-Metrics-using-Adversarial-Behavioral-Analysis
https://github.com/badhandas/hw_synthesis
https://github.com/baichuan-inc/Baichuan-Omni-1.5
https://github.com/baichuan-inc/baichuan-audio
https://github.com/bangoc123/drop-rag
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/bangoc123/retrieval-backend-with-rag
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/bangoc123/transformer
https://github.com/baosongyang/WRD
https://github.com/baotramduong/Sentiment-Analysis-with-Deep-Learning-using-BERT
https://github.com/baotramduong/Twitter-Sentiment-Analysis-with-Deep-Learning-using-BERT
https://github.com/bartzi/chainer-transformer
https://github.com/basile-bron/Artificial-Intelligence-Lexical
https://github.com/baskargroup/Arboretum
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/baskargroup/biotrove
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/bcmi220/esc4nmt
https://github.com/bdusell/stack-attention
https://github.com/bengittelson/sow-reap-paraphrasing
Neural Syntactic Preordering for Controlled Paraphrase Generation
https://github.com/benjamintaiwo/Attention
https://github.com/bentrevett/pytorch-seq2seq
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/beresandras/gan-flavours-keras
https://github.com/bespontaneous/proteus-pytorch
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/bfopengradient/NLP_text_summarization_apps
https://github.com/bgenchel/MusicalSeqGAN
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
https://github.com/bgenchel/Reinforcement-Learning-for-Music-Generation
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
https://github.com/bhavitvyamalik/bert
https://github.com/bigai-nlco/cream
AR-Diffusion: Auto-Regressive Diffusion Model for Text Generation
https://github.com/binhetech/bert-application
https://github.com/biswa380/t5_multitask_api
Simplifying Paragraph-level Question Generation via Transformer Language Models
https://github.com/biyoml/End-to-End-Mandarin-ASR
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/biyoml/Pytorch-End-to-End-ASR-on-TIMIT
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/bjascob/amrlib
Investigating Pretrained Language Models for Graph-to-Text Generation
https://github.com/bkoch4142/attention-is-all-you-need-paper
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/bluejurand/Kaggle_QA_Google_Labeling
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.