⚗️ distilabel is a framework for synthetic data and AI feedback for AI engineers that require high-quality outputs, full data ownership, and overall efficiency.
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Updated
Jun 11, 2024 - Python
⚗️ distilabel is a framework for synthetic data and AI feedback for AI engineers that require high-quality outputs, full data ownership, and overall efficiency.
The official Python library for Openlayer, the Continuous Model Improvement Platform for AI. 📈
A lightweight library for generating synthetic instruction tuning datasets for your data without GPT.
This Person Might Exist - Synthetic dataset generation
This project allows users to generate synthetic videos from CAD models, including .npy files with additional information. Models are loaded dynamically into a Blender scene, and the camera smoothly moves along spherical points to create the final video.
A framework for prompt tuning using Intent-based Prompt Calibration
Modelling and Inference of MICrobiomes Project (MIMIC) is a Python package dedicated to simulate, model, and predict microbial communities interactions
Large scale simulations made simple.
A framework for generating synthetic genomics data for the evaluation of tumor-only somatic variant calling algorithms.
synth. is a framework designed for the generation of synthetic instructions to enhance LLM training.
Experiments for the paper "Finding patterns in ambiguity", accepted at ReGenAI workshop @ CVPR 2024
Multidimensional cluster generation in Julia
visually create datasets directly within their Jupyter notebooks
Multidimensional cluster generation in Python
Examples scripts that showcase how to use Private AI Text to de-identify, redact, hash, tokenize, mask and synthesize PII in text.
[WACV 2024] AnyStar: Domain randomized universal star-convex 3D instance segmentation
awesome synthetic (text) datasets
The MERIT Dataset is a fully synthetic, labeled dataset created for training and benchmarking LLMs on Visually Rich Document Understanding tasks. It is also designed to help detect biases and improve interpretability in LLMs, where we are actively working. This repository is actively maintained, and new features are continuously being added.
This repository contains documents and source code related to John W. Belanger Smutny's Viriginia Tech Master's of Engineering senior capstone project on "The Effect of Training Published Computer Vision Models on Unreal Engine Synthetic Images". An Introduction to the Synergy between Graphics Rendering Software and Machine Learning.
Generate synthetic clinical study data in the form of individual patients.
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