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* added documentation on how to use unstructured with labelstudio * hard code risk narrative for docs * link to create project call
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ReStructuredText
237 lines
10 KiB
ReStructuredText
Examples
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========
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The following are some examples of how to use the library to parse documents. You can find
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example documents in the
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`example-docs <https://github.com/Unstructured-IO/unstructured/tree/main/example-docs>`_, along
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with instructions on how to download additional documents that are too large to store in the
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repo.
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##########################################
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Sentiment Analysis Labeling in LabelStudio
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##########################################
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The following workflow will show how to format and upload the risk section from an SEC filing
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to LabelStudio for a sentiment analysis labeling task. In addition to the ``unstructured``
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library, this example assumes you have `LabelStudio <https://labelstud.io/guide/#Quick-start>`_
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installed and running locally.
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In addition to the ``unstructured`` library, this examples assumes you have the
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`SEC pipeline <https://github.com/Unstructured-IO/pipeline-sec-filings>`_ repo installed and
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on your Python path, as well `LabelStudio <https://labelstud.io/guide/#Quick-start>`_ installed
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and running locally. First, we'll import dependencies create some dummy risk narrative sections.
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For info on how to pull real SEC documents from EDGAR, see our
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`SEC pipeline <https://github.com/Unstructured-IO/pipeline-sec-filings>`_ repo.
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.. code:: python
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import json
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from unstructured.documents.elements import NarrativeText
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from unstructured.staging.label_studio import (
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stage_for_label_studio,
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LabelStudioAnnotation,
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LabelStudioPrediction,
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LabelStudioResult,
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)
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risk_section = [NarrativeText(text="Risk section 1"), NarrativeText(text="Risk section 2")]
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Next, we'll prepopulate some annotations and predictions. Annotations and predictions are optional.
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If you added annotations, the labeling examples will be pre-labeled in the LabelStudio UI. Predictions
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are used for active learning in LabelStudio. If you include predictions, they will help determine
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the order in which labeled examples are presented to annotators. Feel free to skip this step if you do
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not need either of these features for your labeling task:
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.. code:: python
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annotations = []
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for element in risk_section:
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annotations.append([LabelStudioAnnotation(
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result=[
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LabelStudioResult(
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type="choices",
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value={"choices": ["Positive"]},
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from_name="sentiment",
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to_name="text",
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)
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]
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)]
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)
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predictions = []
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for element in risk_section:
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predictions.append([LabelStudioPrediction(
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result=[
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LabelStudioResult(
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type="choices",
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value={"choices": ["Positive"]},
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from_name="sentiment",
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to_name="text",
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)
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],
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score=0.68
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)]
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)
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Finally, we'll format the data for upload to LabelStudio. You can omit the ``annotations``
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and ``predictions`` kwargs if you did't generated annotations or predictions.
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.. code:: python
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label_studio_data = stage_for_label_studio(
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elements=risk_section,
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annotations=annotations,
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predictions=predictions,
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text_field="text",
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id_field="id"
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)
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# The resulting JSON file is ready to be uploaded to LabelStudio
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with open("label-studio.json", "w") as f:
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json.dump(label_studio_data, f, indent=4)
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At this point, you can go into the LabelStudio UI and select ``Create`` to create a new project.
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Upload your sample ``label-studio.json`` file and select ``Text Classification`` for your
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labeling setup, and you're good to go.
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You can also create a new project in LabelStudio through
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the API by running the following command. Hit ``Account & Settings`` under your user name to find your
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API token. First, use the `create project <https://labelstud.io/api#operation/api_projects_create>`_ call to
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create a new project.
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After creating a project, upload data using the following command. The project ID will come from the
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response of the create project call. For existing projects, you can find the project ID in the URL for
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the project.
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.. code:: bash
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curl -H 'Authorization: Token ${LABELSTUDIO_TOKEN}' \
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-X POST 'http://localhost:8080/api/projects/{project_id}/import' \
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-F 'file=@label-studio.json'
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At this point, you're good to go to start labeling in the LabelStudio UI.
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###########
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PDF Parsing
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###########
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Once installed, you can try the following using the
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`layoutparser <https://arxiv.org/pdf/2103.15348.pdf>`_ paper as an example. The PDF
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of the paper is available in the
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`example-docs <https://github.com/Unstructured-IO/unstructured/tree/main/example-docs>`_ directory.
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.. code:: python
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from unstructured.documents.pdf import PDFDocument
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doc = PDFDocument.from_file("example-docs/layout-parser-paper.pdf")
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print(doc)
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At this point, ``print(doc)`` will print out a string representation of the PDF file. The
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first page of output looks like the following:
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.. code:: python
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"""
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LayoutParser : A Unified Toolkit for Deep Learning Based Document Image Analysis
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Zejiang Shen 1 ( (cid:0) ), Ruochen Zhang 2 , Melissa Dell 3 , Benjamin Charles Germain Lee 4 , Jacob Carlson 3 , and
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Weining Li 5
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Abstract. Recent advances in document image analysis (DIA) have been primarily driven by the application of neural
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networks. Ideally, research outcomes could be easily deployed in production and extended for further investigation.
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However, various factors like loosely organized codebases and sophisticated model configurations complicate the easy
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reuse of im- portant innovations by a wide audience. Though there have been on-going efforts to improve reusability and
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simplify deep learning (DL) model development in disciplines like natural language processing and computer vision, none
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of them are optimized for challenges in the domain of DIA. This represents a major gap in the existing toolkit, as DIA
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is central to academic research across a wide range of disciplines in the social sciences and humanities. This paper
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introduces LayoutParser , an open-source library for streamlining the usage of DL in DIA research and applica- tions.
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The core LayoutParser library comes with a set of simple and intuitive interfaces for applying and customizing DL models
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for layout de- tection, character recognition, and many other document processing tasks. To promote extensibility,
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LayoutParser also incorporates a community platform for sharing both pre-trained models and full document digiti- zation
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pipelines. We demonstrate that LayoutParser is helpful for both lightweight and large-scale digitization pipelines in
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real-word use cases. The library is publicly available at https://layout-parser.github.io
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Keywords: Document Image Analysis · Deep Learning · Layout Analysis · Character Recognition · Open Source library ·
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Toolkit.
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Introduction
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Deep Learning(DL)-based approaches are the state-of-the-art for a wide range of document image analysis (DIA) tasks
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including document image classification [11,
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"""
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The ``Document`` has a ``pages`` attribute consisting of ``Page`` object and the ``Page`` object
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has an ``elements`` attribute consisting of ``Element`` objects. Sub-types of the ``Element`` class
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represent different components of a document, such as ``NarrativeText`` and ``Title``. You can use
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these normalized elements to zero in on the components of a document you most care about.
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############
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HTML Parsing
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############
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You can parse an HTML document using the following command.
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.. code:: python
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from unstructured.documents.html import HTMLDocument
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doc = HTMLDocument.from_file("example-docs/example-10k.html")
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print(doc.pages[2])
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You can also instantiate a document directly from an HTML string using the ``from_string`` method.
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The output of this will be the following:
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.. code:: python
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"""
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SPECIAL NOTE REGARDING FORWARD-LOOKING STATEMENTS
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This report contains statements that do not relate to historical or current facts but are “forward-looking” statements. These statements relate to analyses and other information based on forecasts of future results and estimates of amounts not yet determinable. These statements may also relate to future events or trends, our future prospects and proposed new products, services, developments or business strategies, among other things. These statements can generally (although not always) be identified by their use of terms and phrases such as anticipate, appear, believe, could, would, estimate, expect, indicate, intent, may, plan, predict, project, pursue, will continue and other similar terms and phrases, as well as the use of the future tense.
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Actual results could differ materially from those expressed or implied in our forward-looking statements. Our future financial condition and results of operations, as well as any forward-looking statements, are subject to change and to inherent known and unknown risks and uncertainties. You should not assume at any point in the future that the forward-looking statements in this report are still valid. We do not intend, and undertake no obligation, to update our forward-looking statements to reflect future events or circumstances.
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"""
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If you then run:
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.. code:: python
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doc.pages[2].elements
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You'll get the following output, showing that the parser successfully differentiated between
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titles and narrative text.
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.. code:: python
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[<unstructured.documents.base.Title at 0x169cbe820>,
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<unstructured.documents.base.NarrativeText at 0x169cbe8e0>,
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<unstructured.documents.base.NarrativeText at 0x169cbe3a0>]
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Creating HTML from XML with XSLT
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--------------------------------
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You can also convert XML files to HTML with the appropriate XSLT stylesheet. Note, XSLT
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converts arbitrary XML to XML, so there's no guarantee the result will be HTML. Ensure
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you're using a stylesheet designed to convert your specific XML to HTML. The workflow
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for reading in a document with an XSLT stylesheet is as follows:
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.. code:: python
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from unstructured.document.html import HTMLDocument
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doc = HTMLDocument.from_file(filename="example-docs/factbook.xml",
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stylesheet="example-docs/factbook.xsl")
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If you read from a stylesheet ``HTMLDocument`` will use the ``etree.XMLParser`` by default
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instead of the ``etree.HTMLParser`` because ``HTMLDocument`` assumes you want to convert
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your raw XML to HTML.
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