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/exa-labs/exa-mcp-server
https://github.com/extensive-nlp/TSAI-DeepNLP-END2.0
https://github.com/fabianwinter93/JAX
https://github.com/fabianwinter93/JAX/tree/main/SuperHighPerformanceExtractor
https://github.com/facebookresearch/GENRE
https://github.com/facebookresearch/MultipleAttributeTextRewriting
https://github.com/facebookresearch/ParlAI/tree/master/projects/wizard_of_wikipedia
Wizard of Wikipedia: Knowledge-Powered Conversational agents
https://github.com/facebookresearch/accentor
https://github.com/facebookresearch/bart_ls
https://github.com/facebookresearch/brainmagick
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/facebookresearch/chameleon
https://github.com/facebookresearch/clip-rocket
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/facebookresearch/data2vec_vision
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/facebookresearch/end-to-end-negotiator
Hierarchical Text Generation and Planning for Strategic Dialogue
https://github.com/facebookresearch/flow_matching
https://github.com/facebookresearch/minif2f
Measuring Mathematical Problem Solving With the MATH Dataset
https://github.com/facebookresearch/multimodal
https://github.com/facebookresearch/vissl
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/facebookresearch/xformers
https://github.com/fairseq/fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/faizansuhail89/bert
https://github.com/fanchenyou/deepdiary
https://github.com/fanchenyou/transformer-study
https://github.com/fancyerii/bert
https://github.com/fanqiwan/fuseai
https://github.com/farizrahman4u/seq2seq
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/fastscience-ai/medflamingo
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/fciannel/bert_fciannel
https://github.com/feiwu77777/Handwriting_gen
https://github.com/feiwu77777/Handwriting_generation
https://github.com/feizc/Machine-Translation-Pytorch
https://github.com/felixSchober/ABSA-Transformer
https://github.com/fiabdu/Commonly-Interesting-Images
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/filipbasara0/simple-clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/finnsherry/FlowMatching
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.