Text Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 25
- 평가표
- 155
- 보존된 평가 기록
- 54
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/goldenbili/Bert_Test3
https://github.com/goldenbili/bert_lamb_pretrain
https://github.com/gongrennengzhi/nmt
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/gongshuangshuang/deep-text-corrector
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/goodbai-nlp/amrbart
https://github.com/goodluck4s/bert-
https://github.com/google-deepmind/proactive_t2i_agents
Evaluating Text-to-Visual Generation with Image-to-Text Generation
https://github.com/google-research-datasets/attributed-qa
https://github.com/google/flax/tree/master/examples/wmt
https://github.com/google/t5patches
https://github.com/googlx/lasertagger
https://github.com/gooofy/zbrain
https://github.com/gooppe/transformer-summarization
https://github.com/gordicaleksa/pytorch-original-transformer
https://github.com/gotutiyan/gec-metrics
https://github.com/graykode/commit-autosuggestions
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
https://github.com/graykode/gpt-2-Pytorch
https://github.com/graykode/nlp-tutorial
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/gregor-ge/mblip
https://github.com/greydanus/scribe
https://github.com/griff4692/calibrating-summaries
https://github.com/grzego/handwriting-generation
https://github.com/gsh2014/grammar-coarse2fine
A Syntactic Neural Model for General-Purpose Code Generation
https://github.com/guanliu321/CNN-RNN-HAN-for-Text-Classification-Using-NLP
A Hierarchical Neural Autoencoder for Paragraphs and Documents
https://github.com/guihuzhang/FactSpotter
https://github.com/guillaume-chevalier/Linear-Attention-Recurrent-Neural-Network
https://github.com/guinmoon/llmfarm
https://github.com/gulshanBakle/Neural-Image-Caption-generator
https://github.com/guoyaohua/BERT-Chinese-Annotation
https://github.com/guptakhil12/show-tell
https://github.com/guxm2021/MM_ALT
https://github.com/guzhang480/Google_BERT
https://github.com/h2oai/h2ogpt
https://github.com/h3lio5/linguistic-style-transfer-pytorch
Disentangled Representation Learning for Non-Parallel Text Style Transfer
https://github.com/h4ste/oscar
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.