rm test changes

best-practices/tutorials/fallbacks.mdx

@@ -2,7 +2,6 @@
 title: "Using fallbacks"
 ---
 
-TEST CHANGE
 
 Use Sensible's fallback mechanisms to solve missing or inaccurately extracted data. You can set fallbacks at different levels of granularity: 
 

best-practices/tutorials/go-live.mdx

@@ -2,7 +2,6 @@
 title: "Go-live checklist"
 ---
 
-TEST CHANGE
 
 You've written your configs and tested your reference documents and you're ready to move to production. Here's a handy checklist before you go live:
 

best-practices/tutorials/handwriting.mdx

@@ -2,7 +2,6 @@
 title: "Extracting handwriting and OCR text"
 ---
 
-TEST CHANGE
 
 
 This topic contains tips and tricks for extracting handwriting and OCR'd text, for example from scanned documents or images:

best-practices/tutorials/performance.mdx

@@ -2,7 +2,6 @@
 title: "Optimizing extraction performance"
 ---
 
-TEST CHANGE
 
 To improve extraction performance, you can optimize:
 

layout-based-extractions/getting-started.mdx

@@ -2,7 +2,6 @@
 title: "Getting started with layout-based extractions"
 ---
 
-TEST CHANGE
 
 In this tutorial, you'll learn to extract data out of a set of similar documents using a layout-based query language, SenseML. You'll write JSON to tell Sensible about which data to extract from an example document, using what you know about the layout of the document. SenseML uses a mix of techniques, including machine learning, heuristics, and rules, to extract your target information.
 

layout-based-extractions/library-quickstart.mdx

@@ -2,7 +2,6 @@
 title: "Out-of-the-box extractions"
 ---
 
-TEST CHANGE
 
 If you want to extract from any of the following document types, you can get started in minutes using the [Sensible configuration library](https://github.com/sensible-hq/sensible-configuration-library). The library provides out-of-the-box support for common business forms:
 

layout-based-extractions/portfolio.mdx

@@ -2,8 +2,6 @@
 title: "Multi-document extractions"
 ---
 
-TEST CHANGE
-
 Sometimes a single file contains multiple documents (a "portfolio"). For example, a portfolio file can contain an invoice, a tax document, and a contract. 
 
 Sensible recommends extracting each document in a portfolio using its own document type, so you can write [validations](/best-practices/validate-extractions) for each type. For example, use an "income tax" doc type and an "invoice" doc type, rather than creating a "combined\_tax\_and\_invoice" doc type.

openapi_classification.yml

@@ -96,9 +96,6 @@ paths:
       operationId: classify-document-sync
       summary: Classify document by type (sync)
       description: |
-        
-        TEST CHANGE
-
         **Note:** Use this Classify endpoint for testing. Use the asynchronous Classify endpoint for production.
 
         Score a document's similarity to each document type you defined in your Sensible account. Get scores for the document's similarity to document types and to their reference documents.
@@ -264,7 +261,7 @@ components:
               description: Similarity score comparing the document to the document type, between 0 and 1.
         reference_documents:
           type: array
-          description: TEST CHANGE Reference documents uploaded to the Sensible account that this document is most similar to.
+          description: Reference documents uploaded to the Sensible account that this document is most similar to.
           items:
             type: object
             properties:

senseml-reference/advanced-computed-field-methods/add-computed-fields.mdx

@@ -2,9 +2,6 @@
 title: "Add computed fields"
 ---
 
-
-TEST CHANGE
-
 Enables transforming a table by adding an array of computed fields that operate on the extracted table. For example, you can add text to each cell in a row, concatenate columns, add or remove columns, and so forth. This method is similar to adding a [computed fields array to sections](/senseml-reference/sections/sections-example-copy-to-section). In detail, this method:
 
 * Automatically [zips](/senseml-reference/computed-field-methods/zip) the source table. After the zip, each table cell is a field whose ID is the column heading. Note that zipped tables have the same JSON output structure as sections. This enables you to use section-specific computed fields to transform the table, such as the Copy To Section method.

welcome/getting-started-ai.mdx

@@ -2,8 +2,6 @@
 title: "Getting started"
 ---
 
-here is a test change
-
 ### Overview
 
 Welcome! Sensible is a developer-first platform for extracting structured data from documents, for example, business forms in PDF format. It's highly configurable: you can get simple data in minutes by leveraging GPT-4 and other large-language models (LLMs), or you can tackle complex and idiosyncratic document formatting with Sensible's powerful layout-based document primitives.