semvec/pypi-packages huggingface dataset:
cgmquantify 0.5
Quantifying glucose and glucose variability from CGM devices
License: MIT

# cgmquantify: python package for analyzing glucose and glucose variability
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

Continuous glucose monitoring (CGM) systems provide real-time, dynamic glucose information by tracking interstitial glucose values throughout the day. Glycemic variability, also known as glucose variability, is an established risk factor for hypoglycemia (Kovatchev) and has been shown to be a risk factor in diabetes complications. Over 20 metrics of glycemic variability have been identified.

Here, we provide functions to calculate glucose summary metrics, glucose variability metrics (as defined in clinical publications), and visualizations to visualize trends in CGM data.

#### [User Guide](https://github.com/brinnaebent/cgmquantify/wiki/User-Guide)
#### [Issue Tracking](https://github.com/brinnaebent/cgmquantify/issues)

#### Installation:
* **Recommended:** pip install cgmquantify
* If above does not work: pip install git+git://github.com/brinnaebent/cgmquantify.git
* git clone [repo](https://github.com/brinnaebent/cgmquantify.git)

#### Dependencies: (these will be downloaded upon installation with pip)
pandas, numpy, matplotlib, statsmodels, datetime

>Coming soon -
>* Currently only supports Dexcom CGM, more CGM coming soon
>* Integration with food logs, myFitnessPal food logs
>* Machine Learning methods for discovering trends in CGM data
bert-experimental 1.0.5
Utilities for finetuning BERT-like models
License: MIT
Keywords: bert nlp tensorflow machine learning sentence encoding embedding finetuning

# bert_experimental

Contains code and supplementary materials for a series of Medium articles about the BERT model.

pretraining: https://towardsdatascience.com/pre-training-bert-from-scratch-with-cloud-tpu-6e2f71028379

feature_extraction: https://medium.com/@gaphex/building-a-search-engine-with-bert-and-tensorflow-c6fdc0186c8a

finetuning: https://medium.com/@gaphex/fine-tuning-bert-with-keras-and-tf-module-ed24ea91cff2

representation: https://towardsdatascience.com/improving-sentence-embeddings-with-bert-and-representation-learning-dfba6b444f6b