docs: Add example LabelStudio sentiment analysis example (#24)

* added documentation on how to use unstructured with labelstudio

* hard code risk narrative for docs

* link to create project call

docs/source/examples.rst

@@ -7,6 +7,118 @@ example documents in the
 with instructions on how to download additional documents that are too large to store in the
 repo.
 
+
+##########################################
+Sentiment Analysis Labeling in LabelStudio
+##########################################
+
+The following workflow will show how to format and upload the risk section from an SEC filing
+to LabelStudio for a sentiment analysis labeling task. In addition to the ``unstructured``
+library, this example assumes you have `LabelStudio <https://labelstud.io/guide/#Quick-start>`_ 
+installed and running locally.
+
+
+In addition to the ``unstructured`` library, this examples assumes you have the
+`SEC pipeline <https://github.com/Unstructured-IO/pipeline-sec-filings>`_ repo installed and
+on your Python path, as well `LabelStudio <https://labelstud.io/guide/#Quick-start>`_ installed
+and running locally. First, we'll import dependencies create some dummy risk narrative sections.
+For info on how to pull real SEC documents from EDGAR, see our
+`SEC pipeline <https://github.com/Unstructured-IO/pipeline-sec-filings>`_ repo.
+
+.. code:: python
+
+    import json
+
+    from unstructured.documents.elements import NarrativeText
+    from unstructured.staging.label_studio import (
+        stage_for_label_studio,
+        LabelStudioAnnotation,
+        LabelStudioPrediction,
+        LabelStudioResult,
+    )
+
+    risk_section = [NarrativeText(text="Risk section 1"), NarrativeText(text="Risk section 2")]
+
+Next, we'll prepopulate some annotations and predictions. Annotations and predictions are optional.
+If you added annotations, the labeling examples will be pre-labeled in the LabelStudio UI. Predictions
+are used for active learning in LabelStudio. If you include predictions, they will help determine
+the order in which labeled examples are presented to annotators. Feel free to skip this step if you do
+not need either of these features for your labeling task:
+
+
+.. code:: python
+
+    annotations = []
+    for element in risk_section:
+        annotations.append([LabelStudioAnnotation(
+              result=[
+                  LabelStudioResult(
+                      type="choices",
+                      value={"choices": ["Positive"]},
+                      from_name="sentiment",
+                      to_name="text",
+                  )
+              ]
+          )]
+        )
+
+
+    predictions = []
+    for element in risk_section:
+        predictions.append([LabelStudioPrediction(
+              result=[
+                  LabelStudioResult(
+                      type="choices",
+                      value={"choices": ["Positive"]},
+                      from_name="sentiment",
+                      to_name="text",
+                  )
+              ],
+              score=0.68
+          )]
+        )
+
+
+Finally, we'll format the data for upload to LabelStudio. You can omit the ``annotations``
+and ``predictions`` kwargs if you did't generated annotations or predictions.
+
+
+.. code:: python
+
+    label_studio_data = stage_for_label_studio(
+        elements=risk_section,
+        annotations=annotations,
+        predictions=predictions,
+        text_field="text",
+        id_field="id"
+    )
+
+    # The resulting JSON file is ready to be uploaded to LabelStudio
+    with open("label-studio.json", "w") as f:
+        json.dump(label_studio_data, f, indent=4)
+
+
+At this point, you can go into the LabelStudio UI and select ``Create`` to create a new project.
+Upload your sample ``label-studio.json`` file and select ``Text Classification`` for your
+labeling setup, and you're good to go.
+
+
+You can also create a new project in LabelStudio through
+the API by running the following command. Hit ``Account & Settings`` under your user name to find your
+API token. First, use the `create project <https://labelstud.io/api#operation/api_projects_create>`_ call to
+create a new project.
+After creating a project, upload data using the following command. The project ID will come from the
+response of the create project call. For existing projects, you can find the project ID in the URL for
+the project.
+
+.. code:: bash
+
+    curl -H 'Authorization: Token ${LABELSTUDIO_TOKEN}' \
+    -X POST 'http://localhost:8080/api/projects/{project_id}/import' \
+    -F 'file=@label-studio.json'
+
+At this point, you're good to go to start labeling in the LabelStudio UI.
+
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