unstructured-inference 1.5.3

A library for performing inference using trained models.

Open-Source Pre-Processing Tools for Unstructured Data

The unstructured-inference repo contains hosted model inference code for layout parsing models. These models are invoked via API as part of the partitioning bricks in the unstructured package.

Requires Python 3.12+.

Installation

Package

pip install unstructured-inference

Detectron2

Detectron2 is required for using models from the layoutparser model zoo but is not automatically installed with this package. For MacOS and Linux, build from source with:

pip install 'git+https://github.com/facebookresearch/detectron2.git@57bdb21249d5418c130d54e2ebdc94dda7a4c01a'

Other install options can be found in the Detectron2 installation guide.

Windows is not officially supported by Detectron2, but some users are able to install it anyway. See discussion here for tips on installing Detectron2 on Windows.

Development Setup

This project uses uv for dependency management.

# Clone and install all dependencies (including dev/test/lint groups)
git clone https://github.com/Unstructured-IO/unstructured-inference.git
cd unstructured-inference
make install

Run make help for a full list of available targets.

Getting Started

To get started with the layout parsing model, use the following commands:

from unstructured_inference.inference.layout import DocumentLayout

layout = DocumentLayout.from_file("sample-docs/loremipsum.pdf")

print(layout.pages[0].elements)

Once the model has detected the layout and OCR'd the document, the text extracted from the first page of the sample document will be displayed. You can convert a given element to a dict by running the .to_dict() method.

Models

The inference pipeline operates by finding text elements in a document page using a detection model, then extracting the contents of the elements using direct extraction (if available), OCR, and optionally table inference models.

We offer several detection models including Detectron2 and YOLOX.

Using a non-default model

When doing inference, an alternate model can be used by passing the model object to the ingestion method via the model parameter. The get_model function can be used to construct one of our out-of-the-box models from a keyword, e.g.:

from unstructured_inference.models.base import get_model
from unstructured_inference.inference.layout import DocumentLayout

model = get_model("yolox")
layout = DocumentLayout.from_file("sample-docs/layout-parser-paper.pdf", detection_model=model)

Using your own model

Any detection model can be used for in the unstructured_inference pipeline by wrapping the model in the UnstructuredObjectDetectionModel class. To integrate with the DocumentLayout class, a subclass of UnstructuredObjectDetectionModel must have a predict method that accepts a PIL.Image.Image and returns a list of LayoutElements, and an initialize method, which loads the model and prepares it for inference.

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