@@ -50,7 +50,7 @@ To create a custom extraction model, label a dataset of documents with the value
 > Document Intelligence `v4.0 2024-11-30 (GA)` API supports custom neural model **overlapping fields**, **signature detection** 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 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](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. With V4.0, custom neural model supports signature detection, table confidence and overlapping fields. See [neural models](custom-neural.md) to learn more about custom document models.
 
 ### Custom template model
 
@@ -145,12 +145,15 @@ The following table compares custom template and custom neural features:
 |Feature|Custom template (form) | Custom neural (document) |
 |---|---|---|
 |Document structure|Template, form, and structured | Structured, semi-structured, and unstructured|
-|Training time | 1 to 5 minutes | 20 minutes to 1 hour |
+|Training time | 1 to 5 minutes | 30 minutes to 12 hour* |
 |Data extraction | Key-value pairs, tables, selection marks, coordinates, and signatures | Key-value pairs, selection marks, and tables|
 |Overlapping fields | Not supported | Supported |
 |Document variations | Requires a model per each variation | Uses a single model for all variations |
 |Language support | [**Language support custom template**](../language-support/custom.md#custom-template)  | [**Language support custom neural**](../language-support/custom.md#custom-neural) |
 
+
+*-Default training time is 30 mins, enable paid training to train a model longer than 30 mins. Check more details under [training support for custom neural](../train/custom-neural.md) 
+
 ### Custom classification model
 
  Document classification is a new scenario supported by Document Intelligence with the ```2023-07-31``` (v3.1 GA) API. The document classifier API supports classification and splitting scenarios. Train a classification model to identify the different types of documents your application supports. The input file for the classification model can contain multiple documents and classifies each document within an associated page range. To learn more, *see* [custom classification](custom-classifier.md) models.
@@ -249,7 +252,7 @@ This table compares the supported data extraction areas:
 **Table symbols**:<br>
 ✔—Supported<br>
 **n/a—Currently unavailable;<br>
-*-Behaves differently depending upon model. With template models, synthetic data is generated at training time. With neural models, exiting text recognized in the region is selected.
+*-Behaves differently depending upon model. With template models, synthetic data is generated at training time. With neural models, existing text recognized in the region is selected.
 
 > [!TIP]
 > To choose between the two model types, start with a custom neural model if it meets your functional needs. See [custom neural](custom-neural.md) to learn more about custom neural models.