semvec/pypi-packages huggingface dataset:
pyconverters-deeptranscript 0.5.59
DeepTranscript converter.
Requires Python: >=3.8

# pyconverters_deeptranscript

[![license](https://img.shields.io/github/license/oterrier/pyconverters_deeptranscript)](https://github.com/oterrier/pyconverters_deeptranscript/blob/master/LICENSE)
[![tests](https://github.com/oterrier/pyconverters_deeptranscript/workflows/tests/badge.svg)](https://github.com/oterrier/pyconverters_deeptranscript/actions?query=workflow%3Atests)
[![codecov](https://img.shields.io/codecov/c/github/oterrier/pyconverters_deeptranscript)](https://codecov.io/gh/oterrier/pyconverters_deeptranscript)
[![docs](https://img.shields.io/readthedocs/pyconverters_deeptranscript)](https://pyconverters_deeptranscript.readthedocs.io)
[![version](https://img.shields.io/pypi/v/pyconverters_deeptranscript)](https://pypi.org/project/pyconverters_deeptranscript/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pyconverters_deeptranscript)](https://pypi.org/project/pyconverters_deeptranscript/)

My new converter

## Installation

You can simply `pip install pyconverters_deeptranscript`.

## Developing

### Pre-requesites

You will need to install `flit` (for building the package) and `tox` (for orchestrating testing and documentation building):

```
python3 -m pip install flit tox
```

Clone the repository:

```
git clone https://github.com/oterrier/pyconverters_deeptranscript
```

### Running the test suite

You can run the full test suite against all supported versions of Python (3.8) with:

```
tox
```

### Building the documentation

You can build the HTML documentation with:

```
tox -e docs
```

The built documentation is available at `docs/_build/index.html.
minmon 0.9.9
MINimal MONitor - write and graph: date, time, memory, CPU, temperature, disk i/o and net i/o
Requires Python: >=3.6

```
usage: minmon [-h] [-V] [-s SECONDS] [-f FORMAT] [-t]

MINimal MONitor - write and graph: date, time, memory, CPU, temperature, disk i/o and net i/o

MINMON  writes  on  standard  output  at  fixed time intervals a line built
according to the characters in -f --format argument:

    • 'y': Date as 'YYYY-mm-dd'
    • 'h': Time as 'HH:MM:SS'
    • 'm':  Memory  and  Swap  %  usage as two 2-characters decimal numbers
      ('**' = 100)
    • 'M':  Memory  and  Swap  % usage as a 21-characters '0-50-100' linear
      graph ('M' = Memory, 'S' = Swap, 'X' = both)
    • 'c':   CPU  %  usage  and  Temperature  °C  (Celsius  degrees)as  two
      2-characters decimal numbers ('**' = 100)
    • 'C':  CPU  %  usage  and Temperature °C as a 21-characters '0-50-100'
      linear graph ('C' = CPU, 'T' = temperature, 'X' = both)
    • 'r': disk Read and Write B/s (bytes per second) as two human-readable
      5-characters  numbers  ('K' = 2 ** 10 = 1024, 'M' = 2 ** 20 = 1024 **
      2, 'G' = 2 ** 30 = 1024 ** 3, 'T' = 2 ** 40 = 1024 ** 4)
    • 'R':  disk  Read  and Write B/s (bytes per second) as a 19-characters
      '1-K-M-G' logarithmic graph ('R' = Read, 'W' = write, 'X' = both)
    • 'd':  network  Download  and  Upload in B/s (bytes per second) as two
      human-readable 5-characters numbers
    • 'D':  network  Download  and  Upload  in  B/s (bytes per second) as a
      19-characters  '1-K-M-G'  logarithmic  graph  ('D'  = Download, 'U' =
      Upload, 'X' = both)

MINMON is minimal as it has a minimal RAM (6 MB) and CPU footprint.

To stop the program press Ctrl-C.

options:
  -h, --help            show this help message and exit
  -V, --version         show program's version number and exit
  -s SECONDS, --seconds SECONDS
                        interval in seconds as an integer greater than 0
                        (default: '1')
  -f FORMAT, --format FORMAT
                        output format (default: 'yhmMcCrRdD')
  -t, --tera-byte       extended 25-characters '1-K-M-G-T' logarithmic scale
                        (default: 19-characters '1-K-M-G')
```
pyannotators-trankitner 0.5.175
Annotator based on Trankit NER
Requires Python: >=3.8

# pyannotators_trankitner

[![license](https://img.shields.io/github/license/oterrier/pyannotators_trankitner)](https://github.com/oterrier/pyannotators_trankitner/blob/master/LICENSE)
[![tests](https://github.com/oterrier/pyannotators_trankitner/workflows/tests/badge.svg)](https://github.com/oterrier/pyannotators_trankitner/actions?query=workflow%3Atests)
[![codecov](https://img.shields.io/codecov/c/github/oterrier/pyannotators_trankitner)](https://codecov.io/gh/oterrier/pyannotators_trankitner)
[![docs](https://img.shields.io/readthedocs/pyannotators_trankitner)](https://pyannotators_trankitner.readthedocs.io)
[![version](https://img.shields.io/pypi/v/pyannotators_trankitner)](https://pypi.org/project/pyannotators_trankitner/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pyannotators_trankitner)](https://pypi.org/project/pyannotators_trankitner/)

Annotator based on Facebook's TrankitNER

## Installation

You can simply `pip install pyannotators_trankitner`.

## Developing

### Pre-requesites

You will need to install `flit` (for building the package) and `tox` (for orchestrating testing and documentation building):

```
python3 -m pip install flit tox
```

Clone the repository:

```
git clone https://github.com/oterrier/pyannotators_trankitner
```

### Running the test suite

You can run the full test suite against all supported versions of Python (3.8) with:

```
tox
```

### Building the documentation

You can build the HTML documentation with:

```
tox -e docs
```

The built documentation is available at `docs/_build/index.html.
pyannotators-trfclassifier 0.5.103
Classifier based on Huggingface Text Classification pipeline
Requires Python: >=3.8

# pyannotators_trfclassifier

[![license](https://img.shields.io/github/license/oterrier/pyannotators_trfclassifier)](https://github.com/oterrier/pyannotators_trfclassifier/blob/master/LICENSE)
[![tests](https://github.com/oterrier/pyannotators_trfclassifier/workflows/tests/badge.svg)](https://github.com/oterrier/pyannotators_trfclassifier/actions?query=workflow%3Atests)
[![codecov](https://img.shields.io/codecov/c/github/oterrier/pyannotators_trfclassifier)](https://codecov.io/gh/oterrier/pyannotators_trfclassifier)
[![docs](https://img.shields.io/readthedocs/pyannotators_trfclassifier)](https://pyannotators_trfclassifier.readthedocs.io)
[![version](https://img.shields.io/pypi/v/pyannotators_trfclassifier)](https://pypi.org/project/pyannotators_trfclassifier/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pyannotators_trfclassifier)](https://pypi.org/project/pyannotators_trfclassifier/)

Classifier based on Huggingface Text Classification pipeline

## Installation

You can simply `pip install pyannotators_trfclassifier`.

## Developing

### Pre-requesites

You will need to install `flit` (for building the package) and `tox` (for orchestrating testing and documentation building):

```
python3 -m pip install flit tox
```

Clone the repository:

```
git clone https://github.com/oterrier/pyannotators_trfclassifier
```

### Running the test suite

You can run the full test suite against all supported versions of Python (3.8) with:

```
tox
```

### Building the documentation

You can build the HTML documentation with:

```
tox -e docs
```

The built documentation is available at `docs/_build/index.html.