semvec/pypi-packages huggingface dataset:
covpred 1.0.4
A library containing algorithms to model population dynamics resulting from infectious diseases and more.
Requires Python: >=3.6

# EpidemicsRunner API #

**CovPred** is a Python library that provides various algorithms
useful for modeling population dynamics inferred from e.g. infectious diseases. Amongst others, it is used by [CovPred](www.CovPred.de) in this exact form.

# Contents

* [Installation Notes](#installation)
* [Description](#description)



# Installation Notes 
## General
Just run pip-install covpred

# Description
The following algorithms are currently implemented:

|Name|Description|
|---|---|
|SIR|Classical Susceptible-Infected-Recovered ODE Model augmented with a deceased class|
|SEIRD| Extended SIR model accounting for a new class (Exposed) amongst other additions|
|SEIRD_VARA|Extended SIR model paired with variable (over time) infection rate|
|SEIRD_CK|Coupled ODE SEIRD Model to model interaction between the ODE systems|
|SEIRD_PDE| A PDE formulation of the SEIRD model, discretized using Finite Differences|
django-eth-fields 0.0.1
Extension for Django REST framework error display
Requires Python: >=3.6

Django Eth Fields
===================

Helpers for Python Django projects using ethereum


Installation
------------

Using pip


    $ pip install django-eth-fields


Example
-----


    from django_eth_fields.fields import EthereumAddressField, HexField, Uint256Field

    class MyModel(models.Model):
        address = EthereumAddressField()
        hex = HexField()
        unit_255 = Uint256Field()
reconchess 1.6.9
A python package for building Reconnaissance Chess players
Requires Python: >=3.5

reconchess: python package for Reconnaissance Chess
====================================================

Introduction
------------

Reconnaissance Chess is a chess variant (more precisely, a family of chess variants) invented as an R&D project at Johns Hopkins Applied Physics Laboratory (JHU/APL). Reconnaissance Chess adds the following elements to standard (classical) chess: sensing; incomplete information; decision making under uncertainty; coupled management of â€˜battle forcesâ€™ and â€˜sensor resourcesâ€™; and adjudication of multiple, simultaneous, and competing objectives. Reconnaissance chess is a paradigm and test bed for understanding and experimenting with autonomous decision making under uncertainty and in particular managing a network of sensors to maintain situational awareness informing tactical and strategic decision making.

The game implemented in this python package is a relatively basic version using only one kind of sensor that provides perfect information in a small region of the chess board. In the future, extended versions may include noisy sensors of different types; multiple sensing actions per turn; the need to divide attention and resources among multiple, concurrent games; and other complicating factors.

This package includes a "game arbiter" which controls the game flow, maintains the ground truth game board, and notifies players of information collected by sense and move actions.  The package also contains a client API for interacting with the arbiter, which can be used by bot players or other game interfaces.

Installation
------------

::

    pip install reconchess

Documentation
-------------

Documentation is hosted by `Read the Docs <https://reconchess.readthedocs.io/>`_.

License
-------

Distributed under BSD 3-Clause License, for details see LICENSE file.
transformer-srl 2.5.2
SRL Transformer model
Requires Python: >=3.6

![Upload Python Package](https://github.com/Riccorl/srl-bert-verbatlas/workflows/Upload%20Python%20Package/badge.svg)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

# Semantic Role Lableing with BERT

Semantic Role Labeling based on [AllenNLP implementation](https://demo.allennlp.org/semantic-role-labeling) of [Shi et al, 2019](https://arxiv.org/abs/1904.05255). Can be trained using both PropBank and [VerbAatlas](http://verbatlas.org/) inventories and implements also the predicate disambiguation task, in addition to arguments identification and disambiguation.

## How to use

Install the library

```bash
pip install transformer-srl
```

Download the pretrained model `srl_bert_base_conll2012.tar.gz` from [here](https://www.dropbox.com/s/4tes6ypf2do0feb/srl_bert_base_conll2012.tar.gz).

| File | Model | Version | F1 Argument | F1 Predicate |
| :---: | :---: | :---: | :---: | :---: |
| srl_bert_base_conll2012.tar.gz | `bert-base-cased` | 2.4.6 | 86.0 | 95.5 | 

#### CLI

```bash
echo '{"sentence": "Did Uriah honestly think he could beat the game in under three hours?"}' | \
allennlp predict path/to/srl_bert_base_conll2012.tar.gz - --include-package transformer_srl
```

#### Inside Python Code

```python
from transformer_srl import dataset_readers, models, predictors

predictor = predictors.SrlTransformersPredictor.from_path("path/to/srl_bert_base_conll2012.tar.gz, "transformer_srl")
predictor.predict(
  sentence="Did Uriah honestly think he could beat the game in under three hours?"
)
```

## Infos

- Language Model: BERT
- Dataset: CoNLL 2012

### Results with VerbAtlas

With `bert-base-cased`:
```
# Dev set
- F1 arguments 87.6
- F1 predicates 95.5
# Test set
- F1 arguments x
- F1 predicates x
```

With `bert-base-multilingual-cased`:
```
# Dev set
- F1 arguments 86.2
- F1 predicates 94.2
# Test set
- F1 arguments 86.1
- F1 predicates 94.9
```

### To-Dos

- [x] Works with both PropBank and VerbAtlas (infer inventory from dataset reader)
- [ ] Compatibility with all models from Huggingface's Transformers.
        - Now works only with models that accept 1 as token type id 
- [ ] Predicate identification (without using spacy)
kind-wycliffwasonga 0.0.2
For querying documents from Korea Investor's Network for Disclosure(KIND)
Requires Python: >=3.6

# kind

Query Korea Investor's Network for Disclosure(KIND) for Korean companies' published documents.

## Instructions

The package requires that you have Mozilla Firefox, Internet browser, installed.

1. Install:

```
pip install kind-wycliffwasonga
```

## Usage

1. CLI

```sh
$ kind search --company 241560
```

```sh
$ kind search --company 241560 --start 2021-01-01 --end 2021-03-31
```

2 Library

```python
from datetime import datetime
import kind

start_date = datetime(2021, 1, 1)
end_date = datetime(2021, 3, 31)
results = kind.search("241560", start_date, end_date)
```