semvec/pypi-packages huggingface dataset:
Biomedicalner 0.0.1
Recognize bio-medical entities from a text corpus
License: MIT

Biomedicalner is an Python library built on top of biomedical-ner-all model to recognize bio-medical entities from a corpus or a medical report

| Feature  | Output  |
|---|---|
| Named Entity Recognition | Recognize 84 bio-medical entities |
| PDF Support | feature coming soon...|


## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install Biomedicalner

```bash
pip install Biomedicalner
```

## Usage

### NER with Biomedicalner
```python
# load all the functions
from Biomedicalner.bio_recognizer import ner_prediction

# returns the predicted class along with the probability of the actual EnvBert model
doc = """
	CASE: A 28-year-old previously healthy man presented with a 6-week history of palpitations. 
      The symptoms occurred during rest, 2â€“3 times per week, lasted up to 30 minutes at a time 
      and were associated with dyspnea. Except for a grade 2/6 holosystolic tricuspid regurgitation 
      murmur (best heard at the left sternal border with inspiratory accentuation), physical 
      examination yielded unremarkable findings.
      """

# returns a dataframe output
ner_prediction(corpus=text, compute='gpu') #pass compute='cpu' if using cpu

```


## About
This model is part of the Research topic "AI in Biomedical field" conducted by Deepak John Reji, Shaina Raza. If you use this work (code, model or dataset),

Please cite us and star at: 

## License
[MIT](https://choosealicense.com/licenses/mit/) License
BiomedicalNER 0.0.2
Recognize bio-medical entities from a text corpus
License: MIT

BiomedicalNER is an Python library built on top of biomedical-ner-all model to recognize bio-medical entities from a corpus or a medical report

| Feature  | Output  |
|---|---|
| Named Entity Recognition | Recognize 84 bio-medical entities |
| PDF Support | feature coming soon...|


## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install BiomedicalNER

```bash
pip install BiomedicalNER
```

## Usage

### NER with BiomedicalNER
```python
# load all the functions
from BiomedicalNER.bio_recognizer import ner_prediction

# returns the predicted class along with the probability of the actual EnvBert model
doc = """
	CASE: A 28-year-old previously healthy man presented with a 6-week history of palpitations. 
      The symptoms occurred during rest, 2â€“3 times per week, lasted up to 30 minutes at a time 
      and were associated with dyspnea. Except for a grade 2/6 holosystolic tricuspid regurgitation 
      murmur (best heard at the left sternal border with inspiratory accentuation), physical 
      examination yielded unremarkable findings.
      """

# returns a dataframe output
ner_prediction(corpus=text, compute='gpu') #pass compute='cpu' if using cpu

```


## About
This model is part of the Research topic "AI in Biomedical field" conducted by Deepak John Reji, Shaina Raza. If you use this work (code, model or dataset),

Please cite us and star at: 

## License
[MIT](https://choosealicense.com/licenses/mit/) License
kubecfg 0.0.4
Python library and cli utility to check k8s client configurations
License: MIT

# kubecfg

Python library and cli utility to check k8s client configurations


[![PyPi](https://img.shields.io/pypi/v/kubecfg.svg?style=flat-square)](https://pypi.python.org/pypi/kubecfg)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg?style=flat-square)](https://github.com/ambv/black)


Show configuration
![Features](https://github.com/joaompinto/kubecfg/raw/main/imgs/features.png)

Test connectivity
![Ping](https://github.com/joaompinto/kubecfg/raw/main/imgs/ping.png)

# Install
```sh
pip install kubecfg
```

# Run
```sh
kubecfg
```

# Use the library
This example shows how to use the [HTTPX](https://www.python-httpx.org/) to obtain the version of the API server.

```python
import httpx
from kubecfg.config import KubeConfig

k8s_config = KubeConfig()
k8s_config.load_config()

server, cert, client_ca = k8s_config.get_auth_data()
r = httpx.get(f"{server}/version", cert=cert, verify=client_ca)
r.raise_for_status()
print(r.text)
```