kefirski/pytorch_RVAE
Recurrent Variational Autoencoder that generates sequential data implemented with pytorch
This tool helps researchers and natural language processing practitioners generate novel sentences that resemble a given dataset. It takes a collection of text data as input and produces new, grammatically plausible sentences. This is ideal for those exploring language generation or augmenting existing datasets with synthetic text.
357 stars. No commits in the last 6 months.
Use this if you need to generate new sentences or short text sequences based on patterns learned from an existing corpus.
Not ideal if you require precise control over the semantic content or factual accuracy of the generated text, as the output can sometimes be nonsensical.
Stars
357
Forks
85
Language
Python
License
MIT
Category
Last pushed
Mar 15, 2017
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/kefirski/pytorch_RVAE"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
yaserkl/RLSeq2Seq
Deep Reinforcement Learning For Sequence to Sequence Models
georgian-io/Multimodal-Toolkit
Multimodal model for text and tabular data with HuggingFace transformers as building block for text data
ctr4si/A-Hierarchical-Latent-Structure-for-Variational-Conversation-Modeling
PyTorch Implementation of "A Hierarchical Latent Structure for Variational Conversation...
nurpeiis/LeakGAN-PyTorch
A simple implementation of LeakGAN in PyTorch
facebookresearch/large_concept_model
Large Concept Models: Language modeling in a sentence representation space