semvec/pypi-packages huggingface dataset:
biobert-embedding 0.1.2
Embeddings from BioBERT
Requires Python: >=3.6

# BioBert Embeddings
Token and sentence level embeddings from BioBERT model (Biomedical Domain).

[BERT](https://arxiv.org/abs/1810.04805), published by Google, is conceptually simple and empirically powerful as it obtained state-of-the-art results on eleven natural language processing tasks.  

The objective of this project is to obtain the word or sentence embeddings from [BioBERT](https://github.com/dmis-lab/biobert), pre-trained model by DMIS-lab. BioBERT, which is a BERT language model further trained on PubMed articles for adapting biomedical domain.

Instead of building and do fine-tuning for an end-to-end NLP model, You can directly utilize word embeddings from Biomedical BERT to build NLP models for various downstream tasks eg. Biomedical text classification, Text clustering, Extractive summarization or Entity extraction etc.



## Features
* Creates an abstraction to remove dealing with inferencing pre-trained BioBERT model.
* Require only two lines of code to get sentence/token-level encoding for a text sentence.
* The package takes care of OOVs (out of vocabulary) inherently.
* Downloads and installs BioBERT pre-trained model (first initialization, usage in next section).

## Install
```
pip install biobert-embedding==0.1.1
```

## Example

word embeddings generated are list of 768 dimensional embeddings for each word. <br>
sentence embedding generated is 768 dimensional embedding which is average of each token.

```python
from biobert_embedding.embedding import BiobertEmbedding

text = "Breast cancers with HER2 amplification have a higher risk of CNS metastasis and poorer prognosis."\

# Class Initialization (You can set default 'model_path=None' as your finetuned BERT model path while Initialization)
biobert = BiobertEmbedding()

word_embeddings = biobert.word_vector(text)
sentence_embedding = biobert.sentence_vector(text)

print("Text Tokens: ", biobert.tokens)
# Text Tokens:  ['breast', 'cancers', 'with', 'her2', 'amplification', 'have', 'a', 'higher', 'risk', 'of', 'cns', 'metastasis', 'and', 'poorer', 'prognosis', '.']

print ('Shape of Word Embeddings: %d x %d' % (len(word_embeddings), len(word_embeddings[0])))
# Shape of Word Embeddings: 16 x 768

print("Shape of Sentence Embedding = ",len(sentence_embedding))
# Shape of Sentence Embedding =  768
```
vcu-ui 0.2.3
VCU Pro Web UI
Requires Python: >=3.7

## VCU Pro User Interface

![System](https://img.shields.io/badge/system-VCU%20Pro-blue)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/vcu-ui)
![PyPI](https://img.shields.io/pypi/v/vcu-ui)
[![Build Status](https://www.travis-ci.org/renestraub/vcu-ui.svg?branch=master)](https://www.travis-ci.org/renestraub/vcu-ui)


### Introduction

A minimal Web user interface for the VCU Pro automotive gateway. It displays important status information and allows basic maintenance actions. Written in python, based on bottle webserver.


### Features

* Display of most important system information
  * System date/time, load, temperature
  * Mobile link information (registration state, signal strength, bearer information)
* Determine GSM cell location, including geographic position (uses OpenCellId and OpenStreetMap)
* Execute test ping over mobile network
* Create TCP service for u-Center GNSS tool
* Restart GSM modem
* Restart system (not yet implemented)


### Preview

![Info](https://github.com/renestraub/vcu-ui/raw/master/preview/info.png)


### Requirements

* VCU Pro Hardware with developer image installed
* Python 3.7+



### Quickstart

1. Install the module with `pip install vcu-ui`
1. Start webserver from shell `vcu-ui-start`
1. Open the website with your browser `10.42.0.1`


#### Run from Python

```python
from vcuui.server import run_server

run_server(port=80)
```


#### Installation as systemd service

Create the following service file ```vcu-ui.service``` in ```/usr/lib/systemd/system/vcu-ui.service```. The service file is also available on [Github](https://github.com/renestraub/vcu-ui/blob/master/vcu-ui.service)


```
[Unit]
Description=VCU Pro Minimal WebUI
 
[Service]
Type=simple
ExecStart=/usr/bin/vcu-ui-start
PIDFile=/run/vcu-ui.pid
 
ExecReload=/bin/kill -HUP $MAINPID
Restart=on-failure
RestartSec=10
 
[Install]
WantedBy=multi-user.target
```


Manage the service with the following systemd commands.

```bash
systemctl daemon-reload     # Tell systemd to search for new services
systemctl enable vcu-ui     # Enable service for next startup

systemctl start vcu-ui      # Start service right now
```
appthreat-scan-reports 1.0.1
Library for producing gorgeous html reports from AppThreat scan results. Compatible with SARIF and grafeas format.
Requires Python: >=3.6

# Introduction

This is a library for producing gorgeous html reports from AppThreat scan results. Compatible with SARIF and grafeas format.

## Installation

```bash
pip install appthreat-scan-reports
```

## Usage

This package is internally used by [sast-scan](http://github.com/AppThreat/sast-scan), a free open-source Static Analysis Security Testing tool.