semvec/pypi-packages huggingface dataset:
sparv-word-prediction-plugin 0.3.0
A sparv plugin for computing word neighbours using a BERT model.
Requires Python: >=3.9

# sparv-word-prediction-plugin

[![CI](https://github.com/spraakbanken/sparv-word-prediction-plugin/actions/workflows/ci.yml/badge.svg)](https://github.com/spraakbanken/sparv-word-prediction-plugin/actions/workflows/ci.yml)
[![PyPI version](https://badge.fury.io/py/sparv-word-prediction-plugin.svg)](https://pypi.org/project/sparv-word-prediction-plugin)

Plugin for applying bert masking as a [Sparv](https://github.com/spraakbanken/sparv-pipeline) annotation.

## Install

First, install Sparv, as suggested:

```bash
pipx install sparv-pipeline
```

Then install install `sparv-word-prediction-plugin` with

```bash
pipx inject sparv-pipeline sparv-word-prediction-plugin
```

## Usage

Depending on how many exlicit exports of annotations you have you can decide to use this
annotation exclusively by adding it as the only annotation to export under `xml_export`:

```yaml
xml_export:
    annotations:
        - <token>:word_precition.word-prediction--kb-bert
```

To use it together with other annotations you might add it under `export`:

```yaml
export:
    annotations:
        - <token>:word_prediction.transformer-neighbour
        ...
```

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### Configuration

You can configure this plugin by choosing a huggingface model, huggingface transformer and the number of neighbours to generate.

#### Model

The model defaults to [`KBLab/bert-base-swedish-cased`](https://huggingface.co/KBLab/bert-base-swedish-cased) but can be configured in `config.yaml`:

```yaml
word_prediction:
    model: "KBLab/bert-base-swedish-cased"
```

#### Tokenizer

The tokenizer defaults to [`KBLab/bert-base-swedish-cased`](https://huggingface.co/KBLab/bert-base-swedish-cased) but can be configured in `config.yaml`:

```yaml
word_prediction:
    tokenizer: "KBLab/bert-base-swedish-cased"
```

#### Number of Neighbours

The number of neighbours defaults to `5` but can be configured in `config.yaml`:

```yaml
word_prediction:
    num_neighbours: 5
```
neops_graphql_generated 0.1.0
A low-level generated GraphQL for Neops
Requires Python: >=3.9,<4.0

# Generated Neops GraphQL Client

**ALPHA**

A low level generated graphql client for neops.

This is an low level client and should not be included directly into a project

## Generate a new Cliebt

To generate a new client, execute following steps

```shell
make get-latest-schema
poetry install --no-root
poetry run ariadne-codegen
poetry build
```

## Publish a new client

```shell
# Get API token on pypi
poetry config pypi-token.pypi your-api-token
poetry publish
```
neops_graphql_gen 0.1.0
A low-level generated GraphQL for Neops
Requires Python: >=3.9,<4.0

# Generated Neops GraphQL Client

**ALPHA**

A low level generated graphql client for neops.

This is an low level client and should not be included directly into a project

## Generate a new Cliebt

To generate a new client, execute following steps

```shell
make get-latest-schema
poetry install --no-root
poetry run ariadne-codegen
poetry build
```

## Publish a new client

```shell
# Get API token on pypi
poetry config pypi-token.pypi your-api-token
poetry publish
```
prometheux 0.0.6
Neurosymbolic Chain
Requires Python: >=3.7

# Prometheux

Prometheux is a Python package designed to simplify and automate the process of rule learning from structured data, leveraging the power of the PyClause library. It provides a streamlined interface for loading datasets, executing rule learning algorithms, and managing the resulting rules, all with minimal setup required from the user.

## Features

- Easy integration with PyClause for rule learning.
- Simplified dataset loading and preprocessing.
- Automated generation of rules from structured data.
- Convenient retrieval of learned rules for analysis and application.

## Installation

Before installing Prometheux, you must ensure that PyClause is properly installed in your environment. PyClause requires a C++ (14) compiler on Linux or C++ build tools on Windows.

### Installing PyClause

1. **For Linux Users**: Ensure you have a C++ compiler installed.
2. **For Windows Users**: Ensure you have Microsoft Visual C++ 14.0 or newer installed.

To install PyClause, run the following command:

```bash
pip install git+https://github.com/symbolic-kg/PyClause.git
```

### Installing Prometheux

Prometheux requires Python 3.7 or newer. It is recommended to install Prometheux in a virtual environment to avoid conflicting with system-wide packages.

### Install with pip

To install the latest version of Prometheux, run the following command:

```pip install prometheux```


### Post-Installation

Ensure that you have a C++ compiler installed, as required by PyClause for Linux users, or C++ build tools for Windows users.

## Usage

### Loading Datasets

Specify the path to your dataset specifications in a `specs.yaml` file:

```yaml
datasets:
  - path: "/path/to/your/dataset/train.txt"
```

### Running Rule Learning

Import Prometheux and use it to load datasets and execute rule learning:

```
import prometheux as pmtx

datasets_specs = "/path/to/your/specs.yaml" 
extensional = pmtx.load(datasets_specs)
intensional = pmtx.train(extensional)
print(intensional)
```

This will print the learned rules to the console.
conda-envfile 0.5.2
Inspect/combine conda YAML environment files
Requires Python: >=3.11

[![ci](https://github.com/tdegeus/conda_envfile/workflows/CI/badge.svg)](https://github.com/tdegeus/conda_envfile/actions)
[![Documentation Status](https://readthedocs.org/projects/conda_envfile/badge/?version=latest)](https://conda_envfile.readthedocs.io/en/latest/?badge=latest)
[![pre-commit](https://github.com/tdegeus/conda_envfile/workflows/pre-commit/badge.svg)](https://github.com/tdegeus/conda_envfile/actions)
[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit)
[![Conda Version](https://img.shields.io/conda/vn/conda-forge/conda_envfile.svg)](https://anaconda.org/conda-forge/conda_envfile)

**Documentation: [https://conda_envfile.readthedocs.io](conda_envfile.readthedocs.io)**

# conda_envfile

Provides a Python library, command line tools, and pre-commit tools to manage conda environment files.

## Command line

### conda_envfile_merge

Merge different environment files.
For example:

```bash
conda_envfile_merge -a "extra-library" env1.yml env2.yml > env3.yml
```

merges `env1.yml` and `env2.yml` and adds the package `extra-library` to the merged environment file `env3.yml`.

### conda_envfile_diff

Show the difference between two environment files.
For example:

```bash
conda_envfile_diff env1.yml env2.yml
```

## pre-commit

### conda_envfile_parse

Keep your `environment.yaml` file unique, sorted, and legal in terms of version limitations.
In your `.pre-commit-config.yaml`, add:

```yaml
- repo: https://github.com/tdegeus/conda_envfile
  rev: v0.5.0
  hooks:
  - id: conda_envfile_parse
    files: "environment.yaml"
```

### conda_envfile_pyproject

Keep your `pyproject.toml` and `environment.yaml` in sync.
In your `.pre-commit-config.yaml`, add:

```yaml
- repo: https://github.com/tdegeus/conda_envfile
  rev: v0.5.0
  hooks:
  - id: conda_envfile_pyproject
    files: "environment.yaml"
```

## Python

Combine different version restrictions. For example:

```python
import conda_envfile

list(map(str, conda_envfile.unique("foo >1.2.0", "foo =1.2.*")))
```

which returns

```python
["foo >1.2.0, <1.3.0"]
```