Reverse Dependencies of onnxconverter-common

The following projects have a declared dependency on onnxconverter-common:

• aad2onnx — Python module to convert AAD model to ONNX format
• adapter-transformers — A friendly fork of HuggingFace's Transformers, adding Adapters to PyTorch language models
• aimodelshare — Deploy locally saved machine learning models to a live rest API and web-dashboard. Share it with the world via modelshare.org
• aimodelshare-nightly — Deploy locally saved machine learning models to a live rest API and web-dashboard. Share it with the world via modelshare.org
• azureml-automl-runtime — Contains the ML and non-Azure specific common code associated with running AutoML for public use.
• azureml-train-automl-runtime — Used for automatically finding the best machine learning model and its parameters.
• azureml-training-tabular — Contains ML models, featurizers and scoring code which can either be used with AutoML or standalone.
• cody-adapter-transformers — A friendly fork of HuggingFace's Transformers, adding Adapters to PyTorch language models
• concrete-ml-extensions-hb — Convert trained traditional machine learning models into tensor computations
• dabox-research — Research repository for the DaBox project.
• hipe4ml-converter — Minimal heavy ion physics environment for Machine Learning
• hummingbird-ml — no summary
• jrvc — Libraries for RVC inference
• jshbtf0302 — State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch
• kedro-onnx — Adds ONNX support to Kedro
• keras2onnx — Converts Machine Learning models to ONNX for use in Windows ML
• miping — MiningPersonalityInGerman enables users to train and apply machine learning models on tweets to predict a user's Big 5 personality.
• ml2rt — Machine learning utilities for model conversion, serialization, loading etc
• mlcvzoo-mmdetection — MLCVZoo MMDetection Package
• mw-adapter-transformers — A friendly fork of HuggingFace's Transformers, adding Adapters to PyTorch language models
• pysipfenn — Python toolset for Structure-Informed Property and Feature Engineering with Neural Networks. It offers unique advantages through (1) effortless extensibility, (2) optimizations for ordered, dilute, and random atomic configurations, and (3) automated model tuning.
• shbtf0302 — State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch
• skl2onnx — Convert scikit-learn models to ONNX
• sphinx-summaries — no summary
• tf-shb-gabriel-0302 — State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch
• torch4uie — Create a Python package.
• transformers — State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow
• zetane — The Zetane Engine
• zetane-engine — The Zetane Engine