semvec/pypi-packages huggingface dataset:
itembed 0.5.1
word2vec for itemsets
License: MIT License
Requires Python: >=3.7
Keywords: itemset,word2vec,embedding

# `itembed` — Item embeddings

This is yet another variation of the well-known word2vec method, proposed by Mikolov et al., applied to unordered sequences, which are commonly referred as itemsets.
The contribution of `itembed` is twofold:

 1. Modifying the base algorithm to handle unordered sequences, which has an impact on the definition of context windows;
 2. Using the two embedding sets introduced in word2vec for supervised learning.

A similar philosophy is described by Wu et al. in StarSpace and by Barkan and Koenigstein in item2vec.
`itembed` uses Numba to achieve high performances.


## Getting started

Install from [PyPI](https://pypi.org/project/itembed/):

```
pip install itembed
```

Or install from source, to ensure latest version:

```
pip install git+https://github.com/sdsc-innovation/itembed.git
```

Please refer to the [documentation](http://sdsc-innovation.github.io/itembed) for detailed explanations and examples.
matrx 2.3.3
A Python package for the rapid development and evaluation of human-agent teaming concepts.
License: MIT License

# The MATRX Software

[![DOI](https://zenodo.org/badge/243515218.svg)](https://zenodo.org/badge/latestdoi/243515218)

## Human-Agent Teaming Rapid Experimentation Software

The field of human-agent teaming (HAT) research aims to improve the collaboration between humans and intelligent agents. Small tasks are often designed to do research in agent development and perform evaluations with human experiments. Currently there is no dedicated library of such tasks. Current tasks are build and maintained independent of each other, making it difficult to benchmark research or explore research to different type of tasks.

<img src="https://www.matrx-software.com/wp-content/uploads/2020/02/matrx_logo.svg" height="100">

To remedy the lack of a team task library for HAT research, we developed the Human-Agent Teaming Rapid Experimentation Software package, or MATRX for short. MATRX’s main purpose is to provide a suite of team tasks with one or multiple properties important to HAT research (e.g. varying team size, decision speed or inter-team dependencies). In addition to these premade tasks, MATRX facilitates the rapid development of new team tasks. 

Also, MATRX supports HAT solutions to be implemented in the form of [Team Design Patterns](https://s3.amazonaws.com/academia.edu.documents/61212125/TeamPatterns_CR_v520191114-20876-1u5752p.pdf?response-content-disposition=inline%3B%20filename%3DTeam_Design_Patterns.pdf&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWOWYYGZ2Y53UL3A%2F20200221%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20200221T092845Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=6188328a97b458647775b65c0b0669c0b56e44f707d49e7398dcf40849181a6e) (TDP). This allows for the creation of a TDP library which structures HAT research by mapping task properties, solutions and obtained results in such a way that identifies research gaps. Perhaps more importantly, it allows for system designers to search for a concrete and evaluated solution to their issues related to HAT.

This all is made possible by MATRX. 

Feel free to try some tasks or to browse our official [webpage](https://matrx-software.com/). This also includes a set of elaborate [tutorials](https://matrx-software.com/tutorials/), [documentation](http://docs.matrx-software.com/en/latest/) and [contribution guide](https://matrx-software.com/contribution_guide/).
.

## Citation
If you use this software in your work, consider citing it as follows:

  _van der Waa, J.S & Haije, T (2023). MATRX: Human Agent Teaming Rapid Experimentation software. Zenodo._

    @software{matrx_2023,
    author       = {Jasper van der Waa, Tjalling Haije},
    title        = {MATRX: Human Agent Teaming Rapid Experimentation software},
    month        = {July},
    year         = {2023},
    publisher    = {Zenodo},
    version      = {2.3.2},
    doi          = {10.5281/zenodo.8154912},
    url          = {https://doi.org/10.5281/zenodo.8154912}}