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Computer Vision
- Semantic Segmentation with Polygons
- Semantic Segmentation with Masks
- Object Detection with Bounding Boxes
- Keypoint Labeling
- Image Captioning
- Optical Character Recognition (OCR)
- Image Classification
- Visual Question Answering
- Object Detection with Ellipses
- Multi-Image Classification
- Multi-page Document Annotation
- Inventory Tracking
- Visual Genome
Natural Language Processing
- Question Answering
- Sentiment Analysis Text Classification
- Named Entity Recognition
- Taxonomy
- Relation Extraction
- Text Summarization
- Machine Translation
Audio/Speech Processing
- Automatic Speech Recognition
- Sound Event Detection
- Automatic Speech Recognition using Segments
- Signal Quality Detection
- Speaker Diarization
- Dialogue Analysis
- Intent Classification
- Audio Classification
- Audio Classification with Segments
- Voice Activity Detection
Conversational AI
- Response Generation
- Response Selection
- Coreference Resolution and Entity Linking
- Slot Filling and Intent Classification
Ranking and Scoring
- Pairwise Regression
- Document Retrieval
- Pairwise Classification
- Content-based Image Retrieval
- Website Rating
- ASR Hypotheses Selection
- Text-to-Image Generation
- Search Page Ranking
Structured Data Parsing
- Freeform Metadata
- PDF Classification
- Tabular Data
- HTML Entity Recognition
- HTML Classification
Time Series Analysis
- Time Series Forecasting
- Change Point Detection
- Activity Recognition
- Signal Quality
- Outliers and Anomaly Detection
- Time Series Classification
- Time Series Labeling
Videos
- Video Classification
- Video Timeline Segmentation
- Video Object Detection and Tracking
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- Template Gallery - Computer Vision
- Template Gallery - Natural Language Processing
- Template Gallery - Audio/Speech Processing
- Template Gallery - Conversational AI
- Template Gallery - Ranking & Scoring
- Template Gallery - Structured Data Parsing
- Template Gallery - Time Series Analysis
- Template Gallery - Videos
- Template Gallery - Dynamic Labels
Question Answering

If you want to train a question answering machine learning model, use this template to develop a dataset. This template prompts annotators to read a passage of text, then highlight the span of text that answers a specific question.
Interactive Template Preview
Labeling Configuration
<View>
<Header value="Please read the passage" />
<Text name="text" value="$text" granularity="word"/>
<Header value="Select a text span answering the following question:"/>
<Text name="question" value="$question"/>
<Labels name="answer" toName="text">
<Label value="Answer" maxUsage="1" background="red"/>
</Labels>
</View>
About the labeling configuration
All labeling configurations must be wrapped in View tags.
You can add a header to provide instructions to the annotator:
<Header value="Please read the passage" />
Use the Text object tag to display a passage of text to the annotator:
<Text name="text" value="$text" granularity="word"/>
Use the granularity
parameter to ensure that highlighted text spans include complete words.
You can add a header to provide instructions to the annotator:
<Header value="Select a text span answering the following question:"/>
Use another Text object tag to display a second text snippet, in this case, a question about the text, to the annotator:
<Text name="question" value="$question"/>
The name
parameter must be different from the name
used for the first text snippet, and the value
is used to reference a column in a CSV file or a key in a JSON file with the text to display. You can also display static text with the value
argument.
Use the Labels control tag to provide an Answer label for the annotator to use to highlight the answer to the question in the original text passage:
<Labels name="answer" toName="text">
<Label value="Answer" maxUsage="1" background="red"/>
</Labels>
Related tags

If you found an error, you can file an issue on GitHub!