Label Studio Documentation — Import pre-annotated data into Label Studio
Label StudioLabel Studio separates original data, model predictions, and human annotations.
## Label Studio takeaway
Label Studio separates original data, model predictions, and human annotations.
For ClaimFlow AI:
- Document = original uploaded PDF/email
- ExtractionRun.extractedJson = Gemini prediction
- extractedJson should stay immutable
- ReviewDecision.correctedJson = human reviewer corrected output
- ReviewDecision.decision = approve_as_is / edit_and_approve / reject / request_more_info
We do not need Label Studio JSON format because we are building our own review UI.
Using Amazon Augmented AI for Human Review - Amazon SageMaker AI
https://docs.aws.amazon.com/sagemaker/latest/dg/a2i-getting-started-core-components.html?utm_source=chatgpt.com
This image depicts the Amazon A2I built-in workflow with Amazon Textract. On the left, the resources that are required to create an Amazon Textract human review workflow are depicted: an Amazon S3 bucket, activation conditions, a worker task template, and a work team. These resources are used to create a human review workflow, or flow definition. An arrow points right to the next step in the workflow: using Amazon Textract to configure a human loop with the human review workflow. A second arrow points right from this step to the step in which activation conditions specified in the human review workflow are met. This initiates the creation of a human loop. On the right of the image, the human loop is depicted in three steps: 1) the worker UI and tools are generated and the task is made available to workers, 2) workers review input data, and finally, 3) results are saved in Amazon S3.
A human loop is used to create a single human review job. For each human review job, you can choose the number of workers that are sent a task to review a single data object. For example, if you set the number of workers per object to 3 for an image classification labeling job, three workers classify each input image. Increasing the number of workers per object can improve label accuracy.
the conditions under which your human loop is called
Specifies the conditions under which a data object is sent for human review directly in your application.