Text Generation
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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/aengusl/latent-adversarial-training
Defending Against Unforeseen Failure Modes with Latent Adversarial Training
https://github.com/af-ai-center/bert
https://github.com/af-ai-center/nerblackbox
https://github.com/af1tang/personaGPT
Personalizing Dialogue Agents: I have a dog, do you have pets too?
https://github.com/affjljoo3581/GPT2
https://github.com/ag17sep/Image-Captioning-Project
https://github.com/agentdr1/la_mil
https://github.com/agermanidis/OpenGPT-2
https://github.com/ahalterman/ngec
https://github.com/ahmetumutdurmus/awd-lstm
https://github.com/ahmetustun/fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/ahsanabbas123/Transformer
https://github.com/ai-forever/augmentex
https://github.com/ai-forever/ru-clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/ajayjain/vectorascent
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/akanyaani/gpt-2-tensorflow2.0
https://github.com/akanyaani/minGPTF
https://github.com/akashe/Python-Code-Generation
https://github.com/akhilkumarco007/lyrebird-egg-master
https://github.com/albarji/neurowriter
https://github.com/albertotestoni/ndq_visual_objects
https://github.com/alex-f1tor/Image-Caption
https://github.com/algharak/BERTenhance
https://github.com/alibaba-damo-academy/FunASR
https://github.com/alibaba/EasyNLP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/alibaba/graphtranslator
https://github.com/alibabasglab/frcrn
https://github.com/alililia/ms_contrib/tree/main/ascend_transformer
https://github.com/alililia/ms_contrib/tree/main/gpu_transformer
https://github.com/alimpiev/embeddings_persistent_homology
https://github.com/alipay/StructuredLM_RTDT
Generative Pretrained Structured Transformers: Unsupervised Syntactic Language Models at Scale
https://github.com/alirezasalemi7/pr-rag
https://github.com/alisterta/Persian-text-to-speech
https://github.com/aliyun/qwen-dianjin
https://github.com/allenai/CommonGen-Eval
CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.