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unstructured/examples/sec-sentiment-analysis
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Matt Robinson 9b0dbc7026
build(deps): bump dependencies; resolve security issues in example dependencies (#300) 
* bump cryptography version

* re pip-compile for latest versions

* update argilla example requirements

* dependency updates

* bump versions

* pin unstructured-inference due to multithreading issue

* linting, linting, linting

* dependency on one line
2023-02-27 12:45:28 -05:00
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.gitignore
docs: example notebooks from community repo (#187)
2023-01-31 10:37:32 -05:00
fetch.py
Resolve various style issues to improve overall code quality (#282)
2023-02-27 11:30:54 -05:00
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)
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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: example notebooks from community repo (#187)
2023-01-31 10:37:32 -05:00

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.