Update_confidence_values_in_docs (#10891)

Rephrased the paragraph about the allowed confidence values for TEDPolicy and DIETClassifier to reflect the actual allowed values. Therefore, removed the linear_norm because it is no longer supported.

changelog/10798.doc.md

@@ -0,0 +1,2 @@
+Updated the `model_confidence` parameter in `TEDPolicy` and `DIETClassifier`. The `linear_norm` is removed
+as it is no longer supported. 

docs/docs/components.mdx

@@ -1316,12 +1316,10 @@ Intent classifiers assign one of the intents defined in the domain file to incom
     This should help in better generalization of the model to real world test sets.
 
   * `model_confidence`:
-    This parameter allows the user to configure how confidences are computed during inference. It can take two values:
-    * `softmax`: Confidences are in the range `[0, 1]` (old behavior and current default). Computed similarities are normalized with the `softmax` activation function.
-    * `linear_norm`: Confidences are in the range `[0, 1]`. Computed dot product similarities are normalized with a linear function.
-
-    Please try using `linear_norm` as the value for `model_confidence`. This should make it easier to tune fallback thresholds for the [FallbackClassifier](./components.mdx#fallbackclassifier).
-
+    This parameter allows the user to configure how confidences are computed during inference. It can take only 
+    one value as input which is `softmax`. In `softmax`, confidences are in the range `[0, 1]`. The computed 
+    similarities are normalized with the `softmax` activation function.
+    
 
 The above configuration parameters are the ones you should configure to fit your model to your data.
 However, additional parameters exist that can be adapted.
@@ -2596,12 +2594,9 @@ Selectors predict a bot response from a set of candidate responses.
     This should help in better generalization of the model to real world test sets.
 
   * `model_confidence`:
-    This parameter allows the user to configure how confidences are computed during inference. It can take two values:
-    * `softmax`: Confidences are in the range `[0, 1]` (old behavior and current default). Computed similarities are normalized with the `softmax` activation function.
-    * `linear_norm`: Confidences are in the range `[0, 1]`. Computed dot product similarities are normalized with a linear function.
-
-    Please try using `linear_norm` as the value for `model_confidence`. This should make it easier to tune fallback thresholds for the [FallbackClassifier](./components.mdx#fallbackclassifier).
-
+      This parameter allows the user to configure how confidences are computed during inference. It can take only 
+    one value as input which is `softmax`. In `softmax`, confidences are in the range `[0, 1]`. The computed 
+    similarities are normalized with the `softmax` activation function.
 
   The component can also be configured to train a response selector for a particular retrieval intent.
   The parameter `retrieval_intent` sets the name of the retrieval intent for which this response selector model is trained.