tldrstory: AI-powered understanding of headlines and story text
tldrstory is a framework for AI-powered understanding of headlines and text content related to stories. tldrstory applies zero-shot labeling over text, which allows dynamically categorizing content. This framework also builds a txtai index that enables text similarity search. A customizable Streamlit application and FastAPI backend service allows users to review and analyze the data processed.
The following links are example applications built with tldrstory.
The easiest way to install is via pip and PyPI
pip install tldrstory
You can also install tldrstory directly from GitHub. Using a Python Virtual Environment is recommended.
pip install git+https://github.com/neuml/tldrstory
Python 3.6+ is supported
Check out troubleshooting link to help resolve environment-specific install issues.
Configures indexing of content. Currently supports pulling data via the Reddit API. See this link for more information on setting up a Reddit API account, read-only access is all that is needed.
Cron-style string that enables scheduled running of the indexing job. See this link for more information on cron strings.
Where to store model output, path will be created if it doesn't already exist.
api.subreddit: name of subreddit to pull from api.sort: sort type api.time: time range api.queries: list of text queries to run api.ignore: list of url patterns to ignore
Runs a series of Reddit API queries. See PRAW documentation for more details on this.
Label configuration for zero-shot classifier. This configuration sets a category along with a list of topic values.
labels: topic: values: [Label 1, Label 2]
The example above configures the category "Topic" with two possible labels, "Label 1" and "Label 2". Any label can be set here and a large-scale NLP model will be used to categorize input text into those labels.
Configures a txtai index used for searching topics. See txtai configuration for more details on this.
Configures a FastAPI backed interface for pulling indexed data.
Path to a model index.
The default application is powered by Streamlit and driven by a YAML configuration file. The configuration file sets the application name, API endpoint for pulling content, and component configuration. A custom Streamlit application or any other application can be used in place of this to pull content from the API endpoint directly.
API endpoint for pulling content.
Markdown string that is used to build a sidebar description.
queries.name: Queries drop down header queries.values: List of values to use for queries drop down
Configures the query drop down box. This should be a list of pre-canned queries to use. If a value of "Latest" is present, it will query for the last N articles. If a value of "--Search--" is present, it will present another text box to allow entering custom queries.
List of slider filters. This should map to the zero-shot labels configured in the indexing section.
chart.name: Chart name chart.x: Chart x-axis column chart.y: Chart y-axis column chart.scale: Color scale for list of colors chart.colors: List of colors
Allows configuration of a scatter plot that graphs two label points. This chart can be used to plot and apply coloring to applied labels.
"column name": dynamic range of coloring
Data table that shows result details. In addition to default columns, this section allows adding additional columns based on the zero-shot labels applied. The default mode is to show the numeric value of the label but a range of text labels can also be applied.
- [0, 5.0, Label 1, "color: #F00"]
- [5.0, 10.0, Label 2, "color: #0F0"]
The above would output the text "Label 1" in red for values between 0 and 5. Values between 5 and 10 would output the text "Label 2" in green.