https://github.com/kynkaat/role-of-imagenet-classes-in-fid
Learning Transferable Visual Models From Natural Language Supervision
Papers with Code 아카이브
Text Generation
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- 25
- 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/kyuer/language-style-transfer
https://github.com/la-serene/English-German-Translation-System
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/labdac/charlacompling
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/labmlai/annotated_deep_learning_paper_implementations
https://github.com/lamp-benchmark/lamp
https://github.com/lancopku/CVST
https://github.com/lancopku/SMAE
Learning Sentiment Memories for Sentiment Modification without Parallel Data
https://github.com/larspars/word-rnn
https://github.com/laserene/English-German-Translation-System
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/lastmile-ai/aiconfig
https://github.com/lastmile-ai/aiconfig/tree/main/cookbooks/Chain-of-Verification
Chain-of-Verification Reduces Hallucination in Large Language Models
https://github.com/ldulcic/chatbot
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/leehyunjun-seoultech/seoultech
https://github.com/leejieun51/transformer
https://github.com/lehinevych/SAAN
https://github.com/lehoanganh298/BERT-Question-Answering
https://github.com/lemuria-wchen/DialogVED
Personalizing Dialogue Agents: I have a dog, do you have pets too?
https://github.com/lenacabrera/gb_mnmt
https://github.com/lennonzurich/lalala
https://github.com/leob03/Image_captionning
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/leolee99/CLIP_ITM
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/leondiao0427/seas
Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment
https://github.com/leondz/garak
https://github.com/leshanbog/lasertagger
https://github.com/liamdugan/question_generation
Simplifying Paragraph-level Question Generation via Transformer Language Models
https://github.com/liang8qi/data2textwithauxiliarysupervision
https://github.com/libeineu/GPKD
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/libeineu/SDT-Training
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/lidongliang666/cv_deep_learning
https://github.com/lif31up/attention-is-all-you-need
https://github.com/lif31up/extended-BERT-for-low-rank-adaption
https://github.com/liliaellouz/wabi-sabi
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
https://github.com/lina2360/HiSeqGan
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
https://github.com/linjieli222/hero_video_feature_extractor
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/linzhiqiu/clip-flant5
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.