unstructured

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History

### Description
Currently linting only takes place over the base unstructured directory
but we support python files throughout the repo. It makes sense for all
those files to also abide by the same linting rules so the entire repo
was set to be inspected when the linters are run. Along with that
autoflake was added as a linter which has a lot of added benefits such
as removing unused imports for you that would currently break flake and
require manual intervention.

The only real relevant changes in this PR are in the `Makefile`,
`setup.cfg`, and `requirements/test.in`. The rest is the result of
running the linters.

2023-10-17 12:45:12 +00:00

.gitignore

docs: example notebooks from community repo (#187 )

2023-01-31 10:37:32 -05:00

fetch.py

refactoring linting (#1739 )

2023-10-17 12:45:12 +00:00

README.md

Resolve numerous typos (#280 )

2023-02-24 17:48:23 -08:00

requirements.in

build(deps): bump dependencies; resolve security issues in example dependencies (#300 )

2023-02-27 12:45:28 -05:00

requirements.txt

build(deps): bump dependencies; resolve security issues in example dependencies (#300 )

2023-02-27 12:45:28 -05:00

sec-sentiment-analysis.ipynb

docs: update sec-sentiment-analysis.ipynb (#342 )

2023-03-06 15:16:14 +00:00

README.md

SEC Sentiment Analysis Model

This directory contains an example of how to use the SEC API, the Unstructured SEC pipeline API, and several bricks from the unstructured library to train a sentiment analysis model for the risk factors section of S-1 filings. To get started, use the following steps:

Ensure you have Python 3.8 or higher installed on your system
Create a new Python virtual environment
Run pip install -r requirements.txt to install the dependencies
Run PYTHONPATH=. jupyter notebook from this directory to launch the notebook

At this point, you'll be able to run the sentiment analysis example notebook.