Examples
The following are some examples of how to use the library to parse documents. You can find example documents in the example-docs, along 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
installed and running locally.
In addition to the unstructured library, this examples assumes you have the
SEC pipeline repo installed and
on your Python path, as well LabelStudio 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 repo.
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:
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.
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 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.
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.
Extracting Metadata from Documents
The unstructured library includes utilities for extracting metadata from
documents. Currently, there is support for extracting metadata from .docx,
.xlsx, and .jpg documents. When you call these functions, the return type
is a Metadata data class that you can convert to a dictionary by calling the
to_dict() method. If you extract metadata from a .jpg document, the output
will include EXIF metadata in the exif_data attribute, if it is available.
Here is an example of how to use the metadata extraction functionality:
from unstructured.file_utils.metadata import get_jpg_metadata
filename = "example-docs/example.jpg"
metadata = get_jpg_metadata(filename=filename)
You can also pass in a file-like object with:
from unstructured.file_utils.metadata import get_jpg_metadata
filename = "example-docs/example.jpg"
with open(filename, "rb") as f:
metadata = get_jpg_metadata(file=f)
To extract metadata from .docx or .xlsx, use get_docx_metadata and
get_xlsx_metadata. The interfaces are the same as get_jpg_metadata.
Exploring Source Documents
The unstructured library includes tools for helping you explore source documents.
To get a summary of the size (in bytes) and type of documents in a directory, you can
use the get_directory_file_info function, as show below. The function will
recursively explore files in subdirectories.
from unstructured.file_utils.exploration import get_directory_file_info
file_info = get_directory_file_info("example-docs")
file_info.filetype.value_counts()
The output (file_info) is a pandas DataFrame.
The result should look similar to:
FileType.EML 4
FileType.TXT 3
FileType.HTML 2
FileType.XML 2
FileType.PDF 2
FileType.DOCX 1
FileType.PPTX 1
FileType.XLSX 1
FileType.JPG 1
Name: filetype, dtype: int64
You can also find the average file size by file type by using the following command
from unstructured.file_utils.exploration import get_directory_file_info
file_info = get_directory_file_info("example-docs")
file_info.groupby("filetype").mean()
The output should look similar to the following:
filesize
filetype
FileType.DOCX 3.660200e+04
FileType.EML 1.490885e+05
FileType.HTML 1.228404e+06
FileType.JPG 3.276400e+04
FileType.PDF 2.429245e+06
FileType.PPTX 2.832900e+04
FileType.TXT 6.113333e+02
FileType.XLSX 4.765000e+03
FileType.XML 7.135000e+02