The MediaPipe Text Classifier task lets you classify text into a set of defined categories, such as positive or negative sentiment. The categories are determined the model you use and how that model was trained. These instructions show you how to use the Text Classifier with Python.
You can see this task in action by viewing the Web demo. For more information about the capabilities, models, and configuration options of this task, see the Overview.
The example code for Text Classifier provides a complete implementation of this task in Python for your reference. This code helps you test this task and get started on building your own text classification app. You can view, run, and edit the Text Classifier example code using just your web browser.
This section describes key steps for setting up your development environment and code projects specifically to use Text Classifier. For general information on setting up your development environment for using MediaPipe Tasks, including platform version requirements, see the Setup guide for Python.
Text Classifier uses the mediapipe pip package. You can install these dependencies with the following:
$ python -m pip install mediapipe
Import the following classes to access the Text Classifier task functions:
from mediapipe.tasks import python from mediapipe.tasks.python import text
The MediaPipe Text Classifier task requires a trained model that is compatible with this task. For more information on available trained models for Text Classifier, see the task overview Models section.
Select and download a model, and then store it in a local directory:
model_path = '/absolute/path/to/text_classifier.tflite'
Specify the path of the model with the
parameter, as shown below:
base_options = BaseOptions(model_asset_path=model_path)
Create the task
The MediaPipe Text Classifier task uses the
create_from_options function to set up the
create_from_options function accepts values for configuration
options to set the classifier options. You can also initialize the task using
create_from_model_path factory function. The
function accepts a relative or absolute path to the trained model file.
For more information on configuration options, see
The following code demonstrates how to build and configure this task.
base_options = python.BaseOptions(model_asset_path=model_path) options = text.TextClassifierOptions(base_options=base_options) with python.text.TextClassifier.create_from_options(options) as classifier: classification_result = classifier.classify(text)
This task has the following configuration options for Android apps:
|Option Name||Description||Value Range||Default Value|
||Sets the language of labels to use for display names provided in the
metadata of the task's model, if available. Default is ||Locale code||en|
||Sets the optional maximum number of top-scored classification results to return. If < 0, all available results will be returned.||Any positive numbers||
||Sets the prediction score threshold that overrides the one provided in the model metadata (if any). Results below this value are rejected.||Any float||Not set|
||Sets the optional list of allowed category names. If non-empty,
classification results whose category name is not in this set will be
filtered out. Duplicate or unknown category names are ignored.
This option is mutually exclusive with
||Any strings||Not set|
||Sets the optional list of category names that are not allowed. If
non-empty, classification results whose category name is in this set will be filtered
out. Duplicate or unknown category names are ignored. This option is mutually
||Any strings||Not set|
Text Classifier works with text (
str) data. The task handles the data input
preprocessing, including tokenization and tensor preprocessing.
All preprocessing is handled within the
classify function. There is no need
for additional preprocessing of the input text beforehand.
input_text = 'The input text to be classified.'
Run the task
The Text Classifier uses the
classify function to trigger inferences. For text
classification, this means returning the possible categories for the input text.
The following code demonstrates how to execute the processing with the task model.
with python.text.TextClassifier.create_from_options(options) as classifier: classification_result = classifier.classify(text)
Handle and display results
The Text Classifier outputs a
TextClassifierResult object containing a list
of possible categories for the input text. The categories are defined by the
the model you use, so if you want different categories, pick a different model,
or retrain an existing one.
The following shows an example of the output data from this task:
TextClassificationResult: Classification #0 (single classification head): ClassificationEntry #0: Category #0: category name: "positive" score: 0.8904 index: 0 Category #1: category name: "negative" score: 0.1096 index: 1
This result has been obtained by running the BERT-classifier on the input text:
"an imperfect but overall entertaining mystery".