semvec/pypi-packages huggingface dataset:
headliner 1.0.2
Easy training and deployment of seq2seq models.
License: MIT


Headliner is a sequence modeling library that eases the training and 
**in particular, the deployment of custom sequence models** for both researchers and developers. 
You can very easily deploy your models in a few lines of code. It was originally 
built for our own research to generate headlines from news articles. 
That's why we chose the name, Headliner. Although this library was created internally to 
generate headlines, you can also use it for other tasks like machine translations,
text summarization and many more.

Read the documentation at: https://as-ideas.github.io/headliner/

Headliner is compatible with Python 3.6+ and is distributed under the MIT license.
sqltask 0.6.4.3
ETL tool based on SqlAlchemy for building robust ETL pipelies with high emphasis on high data quality
License: MIT

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# SqlTask
SqlTask is an extensible ETL library based on [SqlAlchemy](https://www.sqlalchemy.org/)
to help build robust ETL pipelines with high emphasis on data quality.

Main features of SqlTask:
- Create well documented data models that support iterative
development of both schema and data transformation logic.
- Tightly coupled data quality checking with transformation logic with automatic
creation of visualization-friendly and actionable data quality tables.
- Make use of SQL where practical, especially expensive data filtering 
and aggregation during data extraction.
- Row-by-row data transformation using Python where SQL falls short,
e.g. calling third party libraries or storing state from previous rows.
- Encourage use of modern version control tools and processes, especially GIT.
- Performant data uploading/insertion where supported.
- Easy integration with modern ETL orchestration tools, especially
[Apache Airflow](https://airflow.apache.org/).

**Word of caution:** SqlTask is currently under heavy development, and the
API is expected to change frequently.

# Supported databases

SqlTask supports all databases with a SqlAlchemy
[dialect](https://docs.sqlalchemy.org/en/13/dialects/), with
dedicated support for the following engines:
- Google BigQuery
- MS SQL Server (experimental)
- Postgres
- Sqlite
- Snowflake

Engines not listed above will fall back to using regular inserts.

## Installation instructions

To install SqlTask without any dependencies, simply run

```bash
pip install sqltask
```

To automatically pull in dependencies needed by Snowflake, type
```bash
pip install sqltask[snowflake]
```

Please refer to the [documentation](https://sqltask.readthedocs.io/en/latest/)
on Read The Docs for further information.