semvec/pypi-packages huggingface dataset:
Libreria 0.1.3
Esta libreria se conecta a un arduino mediante protocolo I2C, donde se pueden ingresar comandos para solicitar datos tipo float al arduino

# PowerI2c

PowerI2c es una libreria que establece una conexion I2C entre una raspberry pi 4 y un arduino, donde se pueden consultar 
valores de voltaje, corriente y potencia desde las Raspberry y recibir respuestas del Arduino. Se pueden obtener tanto 
los valores por el terminal como almacenar los valores en una variable.

## Installation

Use el administrador de paquetes [pip](https://pip.pypa.io/en/stable/) para instalar PowerI2C.

```bash
pip install PowerI2C
```

## Usage
La libreria consta de 9 comandos, init accede a un menu desde el cual se pueden obtener los valores de voltaje, corriente o potencia y los comandos con prefijo "get" retornan el valor en float de la variable solicitada, los comandos print enseÃ±an por consola los valores solicitados y por ultimo los comando asociados a Address interactuan con la direccion esclavo a la cual se conectara el medidor de potencia.
```python
from PowerI2C import PowerI2C

PowerI2C.init()   #Ejecuta el codigo tipo menu
PowerI2C.getVoltage() 
PowerI2C.getCurrent()  
PowerI2C.getPower()
PowerI2C.printVoltage()
PowerI2C.printCurrent()
PowerI2C.printPower()
PowerI2C.changeAddress()
PowerI2C.printAddress()
```

## Contributing
Las contribuciones son bienvenidas. Github: https://github.com/mercadoea/POWERMETER

## License
[MIT](https://choosealicense.com/licenses/mit/)
clinitokenizer-samrawal 0.0.2
Sentence tokenizer for text from clinical notes.
Requires Python: >=3.6

# clinitokenizer

`clinitokenizer` is a sentence tokenizer for clinical text to split unstructured text from clinical text (such as Electronic Medical Records) into individual sentences. 

General English sentence tokenizers are often unable to correctly parse medical abbreviations, jargon, and other conventions often used in medical records (see "Motivating Examples" section below). clinitokenizer is specifically trained on medical record data and can perform better in these situations (conversely, for non-domain specific use, using more general sentence tokenizers may yield better results). 

The model has been trained on multiple datasets provided by [i2b2 (now n2c2)](https://n2c2.dbmi.hms.harvard.edu). Please visit the n2c2 site to request access to the dataset.

## Quickstart

```
from clinitokenizer.tokenize import clini_tokenize

text = "He was asked if he was taking any medications. Patient is currently taking 5 m.g. Tylenol."
sents = clini_tokenize(text)
# sents = ['He was asked if he was taking any medications.', 'Patient is currently taking 5 m.g. Tylenol.']
```

You can use clinitokenizer as a drop-in replacement for [nltk's](https://www.nltk.org/api/nltk.tokenize.html) `sent_tokenize` function:

```
# to swap in clinitokenizer, replace the nltk import...
from nltk.tokenize import sent_tokenize

# ... with the following clinitokenizer import:
from clinitokenizer.tokenize import clini_tokenize as sent_tokenize

# and tokenizing should work in the same manner!
nltk_sents = sent_tokenize(text)
```

## Technical Details

clinitokenizer uses a `bert-large` Transformer model fine-tuned on sentences from Electronic Medical Records provided from the [i2b2/n2c2 dataset](https://n2c2.dbmi.hms.harvard.edu). The model has been fine-tuned and is inferenced using the [Simple Transformers library](http://simpletransformers.ai), and the model is hosted on [HuggingFace ](https://huggingface.co).

The model can be run on GPU or CPU, and will automatically switch depending on availability of GPU.

## Tradeoffs and Considerations

`clinitokenizer` uses a large neural network (about 1.2 GB) which will be downloaded and cached on-device on first run. This initial setup may take a few minutes, but should only happen once.

Compared to other off-the-shelf sentence tokenizers (i.e. `nltk`), `clinitokenizer` will run slower (especially on machines without GPU) and consume more memory, so if near-instant tokenization is the goal, using a GPU-based machine or another tokenizer may be better.

`clinitokenizer` is optimized for natural-language text in the clinical domain. Therefore, when tokenizing more general English sentences or for tasks in a different domain, other generalized tokenizers may perform better.