A library for performing inference using trained models.
Open-Source Pre-Processing Tools for Unstructured Data
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
pip install unstructured-inference.
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@e2ce8dc#egg=detectron2'
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.
PaddleOCR is required for table processing for
It should not be installed under MacOS with Apple Silicon cpu.
PaddleOCR should be installed using the following instructions.
pip install "unstructured.PaddleOCR"
To install the repository for development, clone the repo and run
make install to install dependencies.
make help for a full list of install options.
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.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
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", model=model)
Using models from the layoutparser model zoo
UnstructuredDetectronModel class in
unstructured_inference.modelts.detectron2 uses the
faster_rcnn_R_50_FPN_3x model pretrained on DocLayNet, but by using different construction parameters, any model in the
layoutparser model zoo can be used.
UnstructuredDetectronModel is a light wrapper around the
Detectron2LayoutModel object, and accepts the same arguments. See layoutparser documentation for details.
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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