Make predictions with Ready-to-use models
Ready-to-use models are available for text, image, and document data. Each data type has Ready-to-use models that are designed to work best for each use case. Use the following guide to determine which Ready-to-use models you can use with your input data:
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Text data: Sentiment analysis, entities extraction, language detection, personal information detection
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Image data: Object detection in images, text detection in images
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Document data: Expense analysis, identity document analysis, document analysis, document queries
The following screenshot shows you the landing page for Ready-to-use models, which showcases all of the different solutions.
Each Ready-to-use model supports both Single predictions and Batch predictions for your dataset. A Single prediction is when you only need to make one prediction. For example, you have one image from which you want to extract text, or one paragraph of text for which you want to detect the dominant language. A Batch prediction is when you’d like to make predictions for an entire dataset. For example, you might have a CSV file of customer reviews for which you’d like to analyze the customer sentiment, or you might have image files in which you’d like to detect objects.
When you have your data and have identified your use case, choose one of the following workflows to make predictions for your data.
Make predictions for text data
The following procedures describe how to make both single and batch predictions for text datasets. You can use the procedures for the following Ready-to-use model types: sentiment analysis, entities extraction, language detection, and personal information detection.
Note
For sentiment analysis, you can only use English language text.
Single predictions
To make a single prediction for Ready-to-use models that accept text data, do the following:
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In the left navigation pane of the Canvas application, choose Ready-to-use models.
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On the Ready-to-use models page, choose the Ready-to-use model for your use case. For text data, it should be one of the following: Sentiment analysis, Entities extraction, Language detection, or Personal information detection.
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On the Run predictions page for your chosen Ready-to-use model, choose Single prediction.
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For Text field, enter the text for which you’d like to get a prediction.
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Choose Generate prediction results to get your prediction.
In the right pane Prediction results, you receive an analysis of your text in addition to a Confidence score for each result or label. For example, if you chose language detection and entered a passage of text in French, you might get French with a 95% confidence score and traces of other languages, like English, with a 5% confidence score.
The following screenshot shows the results for a single prediction using language detection where the model is 100% confident that the passage is English.
Batch predictions
To make batch predictions for Ready-to-use models that accept text data, do the following:
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In the left navigation pane of the Canvas application, choose Ready-to-use models.
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On the Ready-to-use models page, choose the Ready-to-use model for your use case. For text data, it should be one of the following: Sentiment analysis, Entities extraction, Language detection, or Personal information detection.
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On the Run predictions page for your chosen Ready-to-use model, choose Batch prediction.
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Choose Select dataset if you’ve already imported your dataset. If not, choose Import new dataset, and then you are directed through the import data workflow.
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From the list of available datasets, select your dataset and choose Generate predictions to get your predictions.
After the prediction job finishes running, on the Run predictions
page, you see an output dataset listed under Predictions. This dataset
contains your results, and if you select the More options icon (
), you can Preview the output data. Then, you can
choose Download to download the results.
Make predictions for image data
The following procedures describe how to make both single and batch predictions for image datasets. You can use the procedures for the following Ready-to-use model types: object detection images and text detection in images.
Single predictions
To make a single prediction for Ready-to-use models that accept image data, do the following:
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In the left navigation pane of the Canvas application, choose Ready-to-use models.
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On the Ready-to-use models page, choose the Ready-to-use model for your use case. For image data, it should be one of the following: Object detection images or Text detection in images.
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On the Run predictions page for your chosen Ready-to-use model, choose Single prediction.
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Choose Upload image.
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You are prompted to select an image to upload from your local computer. Select the image from your local files, and then the prediction results generate.
In the right pane Prediction results, you receive an analysis of your image in addition to a Confidence score for each object or text detected. For example, if you chose object detection in images, you receive a list of objects in the image along with a confidence score of how certain the model is that each object was accurately detected, such as 93%.
The following screenshot shows the results for a single prediction using the object detection in images solution, where the model predicts objects such as a clock tower and bus with 100% confidence.
Batch predictions
To make batch predictions for Ready-to-use models that accept image data, do the following:
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In the left navigation pane of the Canvas application, choose Ready-to-use models.
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On the Ready-to-use models page, choose the Ready-to-use model for your use case. For image data, it should be one of the following: Object detection images or Text detection in images.
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On the Run predictions page for your chosen Ready-to-use model, choose Batch prediction.
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Choose Select dataset if you’ve already imported your dataset. If not, choose Import new dataset, and then you are directed through the import data workflow.
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From the list of available datasets, select your dataset and choose Generate predictions to get your predictions.
After the prediction job finishes running, on the Run predictions
page, you see an output dataset listed under Predictions. This dataset
contains your results, and if you select the More options icon (
), you can choose View prediction results to preview
the output data. Then, you can choose Download prediction and download the
results as a CSV or a ZIP file.
Make predictions for document data
The following procedures describe how to make both single and batch predictions for document datasets. You can use the procedures for the following Ready-to-use model types: expense analysis, identity document analysis, and document analysis.
Note
For document queries, only single predictions are currently supported.
Single predictions
To make a single prediction for Ready-to-use models that accept document data, do the following:
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In the left navigation pane of the Canvas application, choose Ready-to-use models.
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On the Ready-to-use models page, choose the Ready-to-use model for your use case. For document data, it should be one of the following: Expense analysis, Identity document analysis, or Document analysis.
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On the Run predictions page for your chosen Ready-to-use model, choose Single prediction.
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If your Ready-to-use model is identity document analysis or document analysis, complete the following actions. If you’re doing expense analysis or document queries, skip this step and go to Step 5 or Step 6, respectively.
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Choose Upload document.
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You are prompted to upload a PDF, JPG, or PNG file from your local computer. Select the document from your local files, and then the prediction results will generate.
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If your Ready-to-use model is expense analysis, do the following:
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Choose Upload invoice or receipt.
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You are prompted to upload a PDF, JPG, PNG, or TIFF file from your local computer. Select the document from your local files, and then the prediction results will generate.
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If your Ready-to-use model is document queries, do the following:
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Choose Upload document.
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You are prompted to upload a PDF file from your local computer. Select the document from your local files. Your PDF must be 1–100 pages long.
Note
If you're in the Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), or Europe (Frankfurt) regions, then the maximum PDF size for document queries is 20 pages.
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In the right side pane, enter queries to search for information in the document. The number of characters you can have in a single query is from 1–200. You can add up to 15 queries at a time.
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Choose Submit queries, and then the results generate with answers to your queries. You are billed once for each submissions of queries you make.
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In the right pane Prediction results, you’ll receive an analysis of your document.
The following information describes the results for each type of solution:
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For expense analysis, the results are categorized into Summary fields, which include fields such as the total on a receipt, and Line item fields, which include fields such as individual items on a receipt. The identified fields are highlighted on the document image in the output.
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For identity document analysis, the output shows you the fields that the Ready-to-use model identified, such as first and last name, address, or date of birth. The identified fields are highlighted on the document image in the output.
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For document analysis, the results are categorized into Raw text, Forms, Tables, and Signatures. Raw text includes all of the extracted text, while Forms, Tables, and Signatures only include information on the form that falls into those categories. For example, Tables only includes information extracted from tables in the document. The identified fields are highlighted on the document image in the output.
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For document queries, Canvas returns answers to each of your queries. You can open the collapsible query dropdown to view a result, along with a confidence score for the prediction. If Canvas finds multiple answers in the document, then you might have more than one result for each query.
The following screenshot shows the results for a single prediction using the document analysis solution.
Batch predictions
To make batch predictions for Ready-to-use models that accept document data, do the following:
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In the left navigation pane of the Canvas application, choose Ready-to-use models.
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On the Ready-to-use models page, choose the Ready-to-use model for your use case. For image data, it should be one of the following: Expense analysis, Identity document analysis, or Document analysis.
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On the Run predictions page for your chosen Ready-to-use model, choose Batch prediction.
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Choose Select dataset if you’ve already imported your dataset. If not, choose Import new dataset, and then you are directed through the import data workflow.
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From the list of available datasets, select your dataset and choose Generate predictions. If your use case is document analysis, continue to Step 6.
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(Optional) If your use case is Document analysis, another dialog box called Select features to include in batch prediction appears. You can select Forms, Tables, and Signatures to group the results by those features. Then, choose Generate predictions.
After the prediction job finishes running, on the Run predictions
page, you see an output dataset listed under Predictions. This dataset
contains your results, and if you select the More options icon (
), you can choose View prediction results to preview
the analysis of your document data.
The following information describes the results for each type of solution:
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For expense analysis, the results are categorized into Summary fields, which include fields such as the total on a receipt, and Line item fields, which include fields such as individual items on a receipt. The identified fields are highlighted on the document image in the output.
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For identity document analysis, the output shows you the fields that the Ready-to-use model identified, such as first and last name, address, or date of birth. The identified fields are highlighted on the document image in the output.
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For document analysis, the results are categorized into Raw text, Forms, Tables, and Signatures. Raw text includes all of the extracted text, while Forms, Tables, and Signatures only include information on the form that falls into those categories. For example, Tables only includes information extracted from tables in the document. The identified fields are highlighted on the document image in the output.
After previewing your results, you can choose Download prediction and download the results as a ZIP file.