semvec/pypi-packages huggingface dataset:
nllegalcit 0.0.4
Python library to find citations to Dutch legal documents in natural language text
Requires Python: >=3.10

# nllegalcit

[![GitHub License](https://img.shields.io/github/license/mastaal/nllegalcit?color=%23003399)](https://github.com/mastaal/nllegalcit/blob/main/LICENSE)
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A Python library to find citations to Dutch legal documents in natural language text.

This library is partially based on the [linkextractor](https://gitlab.com/koop/ld/lx/linkextractor) developed by [KOOP](https://www.koopoverheid.nl/) to implement the Linked Data Overheid (LiDO). The aim of `nllegalcit` is to provide a more generally accessible way to recognize Dutch legal citations in natural language text.

**Please note that this library is currently under development. It will probably not work reliably.**

## Supported citations

The following types of citations are implemented, work in progress, or planned:

| Citation type | Implementation status | Implementation notes |
|---------------|-----------------------|----------------------|
| Kamerstukken (Dutch parliamentary documents)  | ⚠️ Work in progress      | Works reasonably well for modern (>1995) citations following the guidelines. Older citations may work. Simple page number notations work. |
| Handelingen (Dutch parliamentary minutes)   |  ⚠️ Work in progress  | Initial implementation exists, but needs more thorough testing.|
| ECLI case law citations  | ⚠️ Work in progress    | Seems to work, but more testing is needed. Paragraph information is not parsed. |
| Dutch case law other than ECLI | 🗓️ Planned |
| Dutch national laws | 🗓️ Planned |
| Dutch treaties | 🗓️ Planned |
| Dutch local law | 🗓️ Planned |
| EU law | 🗓️ Planned |

## License

Copyright (c) 2023, 2024 Martijn Staal <nllegalcit [a t ] martijn-staal.nl>

Available under the European Union Public License v1.2 (EUPL-1.2), or, at your option, any later version.
UnifiedML 1.0.6
Unified library for intelligence training.
Requires Python: >=3.10.8
Keywords: artificial intelligence,machine learning,deep learning,reinforcement learning,image classification

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  <img width="20%" alt="Text changing depending on mode. Light: 'Light' Dark: 'Dark'" src="https://github.com/AGI-init/UnifiedML/assets/92597756/2d124da5-6f32-4265-9784-69f18117d9b9">
<br><br>
</picture>

# Welcome

See our library [Tributaries](https://github.com/AGI-init/tributaries) for mass-deploying UnifiedML apps on remote servers.

Check out [minihydra / leviathan](https://github.com/Cave-Dwellers-Tree-People/minihydra) for how we handle sys args & hyperparams.

## Install

```console
pip install UnifiedML
```

# What is UnifiedML?

<p align="center">
<a href="https://github.com/AGI-init/Assets/assets/92597756/d92e6b3f-9625-427c-87ef-909b3ec40f08">
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  <source width="40%" media="(prefers-color-scheme: dark)" srcset="https://github.com/AGI-init/UnifiedML/assets/92597756/ee9e96bb-9dfa-41c6-89f4-188502061dbd">
  <img width="40%" alt="Text changing depending on mode. Light: 'Light' Dark: 'Dark'" src="https://github.com/AGI-init/UnifiedML/assets/92597756/9eaa5423-ea9d-4793-9b1a-f043fc81d3c1">
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</a>
</p>

UnifiedML is a toolbox & engine for defining ML tasks and training them individually, or together in a single general intelligence.

#

[See full documentation here.](https://agi-init.github.io/UnifiedML) (Documentation in progress)

[//]: # (TODO: Maybe change "base: import" to "task:" to simplify and perhaps "model:" instead of architecture parts. )


# Get started

To start a train session, use the ```ML``` command:

```console
ML
```

Defaults:

```dataset=MNIST```

```task=classify```

Plots, logs, generated images, and videos are automatically stored in: ```./Benchmarking```.

# Voluntary support

### Sponsoring

This library is built by a single individual from low-income means on the funding of a graduate student wage. [Please support financially by sponsoring if you can](https://github.com/sponsors/AGI-init) and if you have access to compute and resources that you would be willing to contribute, please reach out to me at [slerman12@gmail.com](mailto:slerman12@gmail.com?subject=Contributing).

<img width="60%" alt="Text changing depending on mode. Light: 'Light' Dark: 'Dark'" src="https://github.com/AGI-init/UnifiedML-legacy/assets/92597756/b57fe485-51f4-4244-b1ef-d7c35600dd48">

### Contributing code

This library is built with passion and dedication, and every effort has been made to make it minimal and elegant. The standards for accepted push requests are high, but if you have a push that you would like to make or a contribution that you would like to propose, you may [make a request](https://github.com/AGI-init/UnifiedML/edit/main/README.md) and/or leave a note to me either in [issues](https://github.com/AGI-init/UnifiedML/edit/main/README.md) or by email at [slerman12@gmail.com](mailto:slerman12@gmail.com?subject=Contributing).

Thank you.
print-versions 0.1.0
Package for listing versions of python packages used in a Jupyter notebook.
Requires Python: >=3.8

# print_versions

Package for listing versions of python packages used in a Jupyter notebook.

## Installation

```sh
pip install print-versions
```

## Usage

Print versions in ```requirements.txt``` format:

```python
from print_versions import print_versions

import numpy as np
from pandas import DataFrame

print_versions(globals())

# numpy==1.25.2
# pandas==1.5.3
```

Get versions as a dict:

```python
from print_versions import get_versions

import numpy as np
from pandas import DataFrame

get_versions(globals())

# {'numpy': '1.25.2', 'pandas': '1.5.3'}
Accessive 0.1.0
A Python library for converting between different bioinformatic accession types
Requires Python: >=3.6

# Accessive

Accessive is a Python library designed to facilitate the conversion between various bioinformatic accession types, streamlining the process of working with biological data identifiers across different databases and formats.
Features

- Convert between a wide array of accession types.
- Support for major bioinformatics databases and identifier formats.
- Flexible query options with support for filtering by taxon.

## Supported Accession Types

Accessive supports the following accession types:

- Ensembl
    - Gene (ENSG00000096717)
    - mRNA (ENST00000361390)
    - Proteoform (ENSP00000354689)
- Uniprot 
    - Swissprot (P00750)
    - TrEMBL (A0A024R161)
    - Isoform (P00750-1)
- RefSeq 
    - mRNA (NM_001278)
    - ncRNA (NR_001278)
    - Proteoform (NP_001265)
- Nextprot
    - Gene (NX_P00750)
    - Proteoform (NX_P00750-1)
- Alphafold id (AF-B0QZ35-F1)
- ArrayExpress (E-GEOD-3307)
- BioGRID index
- CCDS (CCDS81469)
- EMBL (AK074805)
- Entrez Gene id
- GeneCards index
- HGNC (HGNC:142929)
- MIM_GENE index (601739)
- PDB (4IG9)
- Pfam (PF02146)
- UCSC (uc057tpn.1)
- UniParc (UPI000015D95A)
- WikiGene index

## Installation

To install Accessive, use pip:

```bash
pip install accessive
```
After installation, install the database via the included utiltiy:

```bash
python -m accessive.database_ops --download
```

Note that the Accessive database requires approximately 500MB of disk space. 

## Usage Example

```
>>> from accessive import Accessive
>>> acc = Accessive()
>>> acc.map(ids=['SIRT1', 'UCP1', 'VGF'], to_types=['uniprot_swissprot', 'uniprot_trembl', 'refseq_mrna'], taxon = 9606)

          uniprot_swissprot uniprot_trembl   refseq_mrna
gene_name                                               
SIRT1                  None         B0QZ35          None
SIRT1                  None         E9PC49  NM_001142498
SIRT1                Q96EB6     A0A024QZQ1     NM_012238
SIRT1                  None         B0QZ35  NM_001314049
VGF                  O15240           None          None
VGF                  O15240           None     NM_003378
UCP1                 P25874           None     NM_021833

```