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/kauravin/Neural-Visual-Image-Caption-Generation-using-RNNs-and-LSTMs
https://github.com/kdr/videorag-mrr2024
https://github.com/kedartatwawadi/NN_compression
https://github.com/kefirski/contiguous-succotash
Improved Variational Autoencoders for Text Modeling using Dilated Convolutions
https://github.com/kefirski/hybrid_rvae
A Hybrid Convolutional Variational Autoencoder for Text Generation
https://github.com/keio-smilab24/RelationalFutureCaptioningModel
Relational Future Captioning Model for Explaining Likely Collisions in Daily Tasks
https://github.com/keiyamashita111/aaproject
https://github.com/kelly2016/multi-label-bert
https://github.com/kenkai21/Image_Captioning
https://github.com/keonlee9420/Parallel-Tacotron2
https://github.com/kevin666aa/flaml
Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference
https://github.com/keyeun/adaptive-vp
https://github.com/kh-kim/simple-nmt
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/khanhptnk/bandit-nmt
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/khushsi/Finetuning_BART_for_FACET_Summarization
https://github.com/khwajawisal/Attention-augmented-Convolutional-Neural-Networks
https://github.com/kiko441500/google_bert
https://github.com/kingcheng2000/bert
https://github.com/kingsaint/Wikidata-Descriptions
https://github.com/kirbiyik/caption-it
https://github.com/kirubarajan/trick
Automatic Detection of Generated Text is Easiest when Humans are Fooled
https://github.com/kklemon/bgan-pytorch
https://github.com/klemens-floege/oneprot
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/knotgrass/attention/blob/main/attn/attention.py
https://github.com/kohakublueleaf/lycoris
https://github.com/koles289/udacity-Image-Captioning
https://github.com/kolloldas/torchnlp
https://github.com/kotu931226/classifier_transformer_pytorch
https://github.com/kpot/keras-transformer
https://github.com/krantirk/BERT-Pretrained-model
https://github.com/kriesbeck/masked-language-prediction
https://github.com/krocki/np-transformer
https://github.com/ksktym/e2e-refined-dataset
https://github.com/ky1994/SpeechRecognition
https://github.com/kyegomez/CM3Leon
Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.