Merge pull request #283286 from laujan/patch-1

prmerger-automator[bot] · web-flow · commit 4c4356f8820e · 2024-08-02T16:36:48.000Z
Update concept-custom.md
diff --git a/articles/ai-services/document-intelligence/concept-custom.md b/articles/ai-services/document-intelligence/concept-custom.md
@@ -54,7 +54,7 @@ To create a custom extraction model, label a dataset of documents with the value
 > Starting with version 4.0 (2024-02-29-preview) API, custom neural models now support **overlapping fields** and **table, row and cell level confidence**.
 >
 
-The custom neural (custom document) model uses deep learning models and  base model trained on a large collection of documents. This model is then fine-tuned or adapted to your data when you train the model with a labeled dataset. Custom neural models support structured, semi-structured, and unstructured documents to extract fields. When you're choosing between the two model types, start with a neural model to determine if it meets your functional needs. See [neural models](concept-custom-neural.md) to learn more about custom document models.
+The custom neural (custom document) model uses deep learning models and  base model trained on a large collection of documents. This model is then fine-tuned or adapted to your data when you train the model with a labeled dataset. Custom neural models support extracting key data fields from structured, semi-structured, and unstructured documents. When you're choosing between the two model types, start with a neural model to determine if it meets your functional needs. See [neural models](concept-custom-neural.md) to learn more about custom document models.
 
 ### Custom template model
 

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`@@ -54,7 +54,7 @@ To create a custom extraction model, label a dataset of documents with the value`
`54`	`54`	`> Starting with version 4.0 (2024-02-29-preview) API, custom neural models now support overlapping fields and table, row and cell level confidence.`
`55`	`55`	`>`
`56`	`56`
`57`		-The custom neural (custom document) model uses deep learning models and base model trained on a large collection of documents. This model is then fine-tuned or adapted to your data when you train the model with a labeled dataset. Custom neural models support structured, semi-structured, and unstructured documents to extract fields. When you're choosing between the two model types, start with a neural model to determine if it meets your functional needs. See [neural models](concept-custom-neural.md) to learn more about custom document models.
	`57`	+The custom neural (custom document) model uses deep learning models and base model trained on a large collection of documents. This model is then fine-tuned or adapted to your data when you train the model with a labeled dataset. Custom neural models support extracting key data fields from structured, semi-structured, and unstructured documents. When you're choosing between the two model types, start with a neural model to determine if it meets your functional needs. See [neural models](concept-custom-neural.md) to learn more about custom document models.
`58`	`58`
`59`	`59`	`### Custom template model`
`60`	`60`