semvec/pypi-packages huggingface dataset:
clinical-sectionizer 1.0.0.1
Document section detector using spaCy for clinical NLP

# Clinical Sectionizer
This package offers a component for tagging clinical section titles in docs. 

# This package is deprecated!
Development for `clinical_sectionizer` has been moved to [medSpaCy](https://github.com/medspacy/medspacy) and should now be installed as:

```bash
pip install medspacy
```

```python
# Option 1: Load with a medspacy pipeline
import medspacy
nlp = medspacy.load(enable=["sectionizer"])
print(nlp.pipe_names)

# Option 2: Manually add to a spaCy model
import spacy
from medspacy.section_detection import Sectionizer
nlp = spacy.load("en_core_web_sm")
nlp.add_pipe(Sectionizer(nlp))
```
[<img src="https://github.com/medspacy/medspacy/raw/master/images/medspacy_logo.png" align="center">](https://github.com/medspacy/medspacy)

Please see the [medSpaCy](https://github.com/medspacy/medspacy) GitHub page for additional information and documentation.
doccano-transformer 1.0.2
Format transformer tool for doccano
Requires Python: >=3.5, <4
Keywords: doccano,annotation,machine learning

# doccano-transformer

[![Codacy Badge](https://api.codacy.com/project/badge/Grade/9fe17d104b644a53a3fe189433d3c797)](https://app.codacy.com/gh/doccano/doccano-transformer?utm_source=github.com&utm_medium=referral&utm_content=doccano/doccano-transformer&utm_campaign=Badge_Grade_Dashboard)
[![Build Status](https://github.com/doccano/doccano-transformer/workflows/CI/badge.svg)](https://github.com/doccano/doccano-transformer/actions)

Doccano Transformer helps you to transform an exported dataset into the format of your favorite machine learning library.

## Supported formats

Doccano Transformer supports the following formats:

* CoNLL 2003
* spaCy

## Install

To install `doccano-transformer`, simply use `pip`:

```bash
pip install doccano-transformer
```

## Examples

### Named Entity Recognition

The following formats are supported:

- CoNLL 2003
- spaCy

```python
from doccano_transformer.datasets import NERDataset
from doccano_transformer.utils import read_jsonl

dataset = read_jsonl(filepath='example.jsonl', dataset=NERDataset, encoding='utf-8')
dataset.to_conll2003(tokenizer=str.split)
dataset.to_spacy(tokenizer=str.split)
```

## Contribution

We encourage you to contribute to doccano transformer! Please check out the [Contributing to doccano transformer guide](https://github.com/doccano/doccano-transformer/blob/master/CONTRIBUTING.md) for guidelines about how to proceed. 

## License

[MIT](https://github.com/doccano/doccano-transformer/blob/master/LICENSE)
cycontext 1.0.3.3
ConText algorithm using spaCy for clinical NLP

# cycontext
A Python implementation of the ConText algorithm for clinical text concept assertion using the spaCy framework.

# This package is deprecated!
Development for `cycontext` has been moved to [medSpaCy](https://github.com/medspacy/medspacy) and should now be installed as:

```bash
pip install medspacy
```

```python
# Option 1: Load with a medspacy pipeline
import medspacy
nlp = medspacy.load()
print(nlp.pipe_names)

# Option 2: Manually add to a spaCy model
import spacy
from medspacy.context import ConTextComponent
nlp = spacy.load("en_core_web_sm")
nlp.add_pipe(ConTextComponent(nlp))
```
[<img src="https://github.com/medspacy/medspacy/raw/master/images/medspacy_logo.png" align="center">](https://github.com/medspacy/medspacy)

Please see the [medSpaCy](https://github.com/medspacy/medspacy) GitHub page for additional information and documentation.
SPARQL2Spark 0.0.5
SPARQL Result to Spark

SPARQL2SparkWrapper
===================

|Documentation Status|

This is a simple module that allows developer to query SPARQL endpoints
and analyze the results with Apache Spark. In particular, if a
**SELECT** query is used, a **DataFrame** is returned, while if a
**CONSTRUCT** query is used, a **GraphFrame** is returned. You can find
the documentation `here <https://sparql2spark.readthedocs.io/>`__.

Build
-----

To generate a ``.whl`` file just type ``make build``, and the file will
be generated under ``dist`` folder.

Test
----

To properly test the library a SPARQL endpoint is needed. Navigate to
``deplyoment`` folder and start a Jena-Fuseky component with the command
``docker-compose up -d``. A simple, hand made, **FOAF** ontology with
few individuals is loaded in Jena-Fuseki. You are encouraged to
experiment with the SPARQL endpoint availabe at
`localhost <http://localhost:3030>`__.

After starting Jena-Fuseki, navigate back to the main folder, and type
``make docker-test`` to start the test phase.

Please, feel free to add more tests and open a pull request 😁

.. |Documentation Status| image:: https://readthedocs.org/projects/sparql2spark/badge/?version=latest
   :target: https://sparql2spark.readthedocs.io/en/latest/?badge=latest