semvec/pypi-packages huggingface dataset:
pycount-mi 0.1.0
Counts the number of instances of words in a text document.
License: MIT
Requires Python: >=3.9,<4.0

# pycount_mi

Counts the number of instances of words in a text document.

## Installation

```bash
$ pip install pycount_mi
```

## Usage

`pycount_mi` can be used to count words in a text file and plot results
as follows:

```python
from pycount_mi.pycount_mi import count_words
from pycounts.plotting import plot_words
import matplotlib.pyplot as plt

file_path = "test.txt"  # path to your file
counts = count_words(file_path)
fig = plot_words(counts, n=10)
plt.show()
```

## Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

## License

`pycount_mi` was created by Mukund Iyer. It is licensed under the terms of the MIT license.

## Credits

`pycount_mi` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).
pycounts-joshsia 0.1.0
Calculate word counts in a text file!
License: MIT
Requires Python: >=3.9,<4.0

# pycounts_joshsia

Calculate word counts in a text file!

## Installation

```bash
$ pip install pycounts_joshsia
```

## Usage

`pycounts` can be used to count words in a text file and plot results as follows:

```python
from pycounts_joshsia.pycounts_joshsia import count_words
from pycounts_joshsia.plotting import plot_words
import matplotlib.pyplot as plt

file_path = "test.txt"  # path to your file
counts = count_words(file_path)
fig = plot_words(counts, n=10)
plt.show()
```

## Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

## License

`pycounts_joshsia` was created by Joshua Sia. It is licensed under the terms of the MIT license.

## Credits

`pycounts_joshsia` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).
QA-Annotator 1.1.0.dev1
Data Annotation Tool for NLP-based Question-Answering systems.
License: MIT


![PyPI - License](https://img.shields.io/pypi/l/QA-Annotator) ![PyPI](https://img.shields.io/pypi/v/QA-Annotator)

# QA-Annotator: Annotation Is All You Need


## About The Project

Making the mammoth task of data annotation for an NLP-based Question-Answering system hassle-free. Just jump right in and start annotating!

## Getting Started
 
### Installation

Install with pip (python 3 required)
   ```sh
   $ pip install QA-Annotator 
   ```
### Start the server
   ```sh
   $ annotator 
   ```
## Contributing

Contributions, suggestions are welcomed! :)

If you like the project, Don't forget to give a star! Thanks!

## License

Distributed under the MIT License. See `LICENSE.txt` for more information.


## Contact

Prateek Yadav - [@impyadav_](https://twitter.com/impyadav_)

## Links

Project Link: [https://pypi.org/project/QA-Annotator/](https://pypi.org/project/QA-Annotator/)
faiss-gpu 1.7.2
A library for efficient similarity search and clustering of dense vectors.
License: MIT
Keywords: search nearest neighbors


Faiss is a library for efficient similarity search and clustering of dense
vectors. It contains algorithms that search in sets of vectors of any size, up
to ones that possibly do not fit in RAM. It also contains supporting code for
evaluation and parameter tuning. Faiss is written in C++ with complete wrappers
for Python/numpy. It is developed by Facebook AI Research.