semvec/pypi-packages huggingface dataset:
pymeth 1.0.1
A python package for using basic math algorithm
License: MIT
Requires Python: >=3.6
Keywords: Math,Algorithm,Binary Search

# pymeth
A python package for basic algorithms

## INSTALLATION

### Installing from source code on GitHub
You can clone this repository locally and install package from the source code.

```sh
git clone https://github.com/saurabhmehta1601/pymeth.git
cd pymeth
python setup.py install
```

### Installing directly with pip
You can use pip to install pymeth with
```
pip install pymeth
```

## Publishing package to PyPI

1. Firstly, check the package version and metadata in setup.py.
2. Create the binary distribution wheel file for the package with
```
python setup.py bdist_wheel
```
3. We will be using `twine` to upload package to PyPI, so if you have not installed it already install it with command :
```
pip install twine==4.0.2
```
4. Now, we will use twine to upload package to PyPI

NOTE: In order to upload python package you will need an account on PyPI with 2Factor Authentication enabled.
``` 
twine upload --repository-url https://upload.pypi.org/legacy/ -u __token__ -p YOUR_ACCESS_TOKEN dist/*
```
python-lsp-jsonrpc 1.1.2
JSON RPC 2.0 server library
License: MIT
Requires Python: >=3.8

# Python JSON RPC Server

A Python 3.8+ server implementation of the [JSON RPC 2.0](http://www.jsonrpc.org/specification) protocol. This library has been pulled out of the [Python LSP Server](https://github.com/python-lsp/python-lsp-server) project.

## Installation

    pip install -U python-lsp-jsonrpc

## Examples

The examples directory contains two examples of running language servers over websockets. `examples/langserver.py` shows how to run a language server in-memory. `examples/langserver_ext.py` shows how to run a subprocess language server, in this case the Python LSP Server.

Start by installing `tornado` and `python-lsp-server`

    pip install python-lsp-server[all] tornado

Then running `python examples/langserver.py` or `python examples/langserver_ext.py` will host a websocket on ``ws://localhost:3000/python``.
indobert-embedding 0.0.1
Text embedding encoder using IndoBERT
License: MIT
Requires Python: >=3.6


Text embedding encoder using [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) as the model.

## Installation

```bash
pip install indobert-embedding
```

## Usage

```python
from indo_bert_embedding import get_embedding

embedding = get_embedding("Saya belajar NLP di Neuversity.")
```

For get text similarity distance:

```python
from indo_bert_embedding import text_similarity

distance = text_similarity("Saya belajar NLP di Neuversity.", "Aku belajar NLP di Universitas Indonesia.")
```

`text_similarity` using cosine similarity to calculate distance.

## Citation

```
@inproceedings{koto2020indolem,
  title={IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP},
  author={Fajri Koto and Afshin Rahimi and Jey Han Lau and Timothy Baldwin},
  booktitle={Proceedings of the 28th COLING},
  year={2020}
}
```