update text labeling

Author: sdgilley

articles/machine-learning/how-to-create-text-labeling-projects.md

@@ -8,7 +8,7 @@ ms.reviewer: sgilley
 ms.service: machine-learning
 ms.subservice: mldata
 ms.topic: how-to
-ms.date: 09/29/2022
+ms.date: 02/03/2023
 ms.custom: data4ml, ignite-fall-2021
 ---
 
@@ -72,39 +72,41 @@ In many cases, it's fine to just upload local files. But [Azure Storage Explorer
 
 To create a dataset from data that you've already stored in Azure Blob storage:
 
-1. Select **Create a dataset** > **From datastore**.
-1. Assign a **Name** to your dataset.
+1. Select **+ Create** .
+1. Assign a **Name** to your dataset, and optionally a description.
 1. Choose the **Dataset type**:
     * Select **Tabular** if you're using a .csv or .tsv file, where each row contains a response.
     * Select **File** if you're using separate .txt files for each response.
-1. (Optional) Provide a description for your dataset.
-1. Select **Next**.
-1. Select the datastore.
+1. Select **Next**
+1. Select **From Azure storage**, then **Next**.
+1. Select the datastore, then select **Next**.
 1. If your data is in a subfolder within your blob storage, choose **Browse** to select the path.
     * Append "/**" to the path to include all the files in subfolders of the selected path.
     * Append "**/*.*" to include all the data in the current container and its subfolders.
-1. Select **Next**.
-1. Confirm the details. Select **Back** to modify the settings or **Create** to create the dataset.
+1. Select **Create**.
+1. Now select the data asset you just created.
 
 ### Create a dataset from uploaded data
 
 To directly upload your data:
 
-1. Select **Create a dataset** > **From local files**.
-1. Assign a **Name** to your dataset.
-1. Choose the **Dataset type**.
-    * Select **Tabular** if you're using a .csv or .tsv file, where each row is a response. 
+1. Select **+ Create**.
+1. Assign a **Name** to your dataset, and optionally a description.
+1. Choose the **Dataset type**:
+    * Select **Tabular** if you're using a .csv or .tsv file, where each row contains a response.
     * Select **File** if you're using separate .txt files for each response.
-1. (Optional) Provide a description of your dataset.
-1. Select **Next**
-1. (Optional) Select or create a datastore. Or keep the default to upload to the default blob store ("workspaceblobstore") of your Machine Learning workspace.
-1. Select **Upload** to select the local file(s) or folder(s) to upload.
 1. Select **Next**.
-1. If uploading .csv or .tsv files:
-    * Confirm the settings and preview, select **Next**.
-    * Include all columns of text you'd like the labeler to see when classifying that row.  If you'll be using ML assisted labeling, adding numeric columns may degrade the ML assist model.
-    * Select **Next**.
-1.  Confirm the details. Select **Back** to modify the settings or **Create** to create the dataset.
+1. Select **From local files**, then select **Next**.
+1. (Optional) Select a datastore. Or keep the default to upload to the default blob store ("workspaceblobstore") of your Machine Learning workspace.
+1. Select **Next**.
+1. Select **Upload > Upload files** or **Upload > Upload folder** to select the local files or folder(s) to upload.
+1. In the browser window, find your files or folder, then select **Open**.
+1. Continue using **Upload** until you have specified all your files/folders.
+1. Check the box **Overwrite if already exists** if you wish.  Verify the list of files/folders.
+1. Select **Next**.
+1. Confirm the details. Select **Back** to modify the settings or **Create** to create the dataset.
+1. Now select the data asset you just created.
+
 
 
 ## Configure incremental refresh