gretel-synthetics
CTGAN
| gretel-synthetics | CTGAN | |
|---|---|---|
| 4 | 2 | |
| 535 | 1,140 | |
| 3.2% | 2.0% | |
| 7.2 | 8.5 | |
| about 24 hours ago | 2 days ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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gretel-synthetics
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Ask HN: If we train an LLM with “data” instead of “language” tokens
Hey there! Co-founder of Gretel.ai here, and I think I can provide some insights on this topic.
Firstly, the concept you're hinting at is not purely traditional ML. In traditional machine learning, we often prioritize feature extraction and engineering specific to a given problem space before training.
What you're describing and what we've been working on at Gretel.ai, is leveraging the power of models like Large Language Models (LLMs) to understand and extrapolate from vast amounts of diverse data without the need for time-consuming feature engineering. Here's a link to our open-source library https://github.com/gretelai/gretel-synthetics for synthetic data generation (currently supporting GAN and RNN-based language models), and also our recent announcement around a Tabular LLM we're training to help people build with data https://gretel.ai/tabular-llm
A few areas where we've found tabular or Large Data Models to be really useful are:
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Libraries for synthetic data?
you can try QuantGAN: https://github.com/PakAndrey/QuantGANforRisk also try DoppelGANger https://github.com/gretelai/gretel-synthetics/tree/master/src/gretel_synthetics/timeseries_dgan
- Which open source tool for generating synthetic data sets?
- Gretel-synthetics: open-source library to create synthetic datasets
CTGAN
What are some alternatives?
Copulas - A library to model multivariate data using copulas.
SDV - Synthetic data generation for tabular data
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