fixes

Author: alelavml3

md-docs/user_guide/task.md

@@ -9,7 +9,7 @@ A Task is associated with a unique identifier that will be used by SDK to operat
 The identifier can be retrieved from the Task homepage or by looking at the url.
 
 A Task has a *status* that summarizes the health of its AI model.
-The status depends on the monitoring module and changes from Ok, Warning or Drift when the Monitoring modules detects drifts on monitored quantities.
+The status depends on the monitoring module and changes from Ok, Warning or Drift when the Monitoring modules detect drifts on monitored quantities.
 
 Moreover, in the Task homepage is present the section named "Data events" which shows the most recent detection events generated by the monitoring module.
 It is possible to click on *view* to see more details or discard the notification (the event will remain available for future analysis on the [Detection] page).
@@ -25,16 +25,20 @@ Generic attributes are:
 | Name | Name of the Task, unique for the Project. |
 | Tags | Optional customizable list of tags. They are used to better describe the Task and to improve search. |
 | Task type | Artificial intelligence type of Task. Possible values are:<br><ul><li>[Regression](task.md#regression)</li><li>[Binary classification](task.md#classification)</li><li>[Multiclass classification](task.md#classification)</li><li>[Multilabel classification](task.md#classification)</li><li>[Retrieval Augmented Generation](task.md#retrieval-augmented-generation)</li><li>[Object Detection](task.md#object-detection)</li></ul>|
-| Data structure | Type of input data the Task uses. Possible values are:<br><ul><li>Tabular: standard table based data used in contexts like regression or classification.</li><li>Image: images in their different formats and channels.</li><li>Text: textual data expressed as strings. When data structure is Text, attribute *Text Language* is required.</li><li>Embeddings: input data are arrays that could represent embedding either image or text data. This data structure is used when raw data are not shared with ML cube Platform.</li></ul> |
+| Data structure | Type of input data the Task uses. Possible values are:<br><ul><li>Tabular: standard table based data used in contexts like regression or classification.</li><li>Image: images in their different formats and channels.</li><li>Text: textual data expressed as strings. When data structure is Text, attribute *Text Language* is required.</li><li>Embedding: input data are arrays that could represent embedding either image or text data. This data structure is used when raw data are not shared with ML cube Platform.</li></ul> |
 |Optional target| Boolean value that specifies if the ground truth is always available or not. In some Tasks, the actual value is not present until explicit labeling is done. In this cases, the Task is marked as with optional target so that ML cube Platform works accordingly. |
 | Cost info | Optional information about costs that depend on Task Type. |
-
+| Text language | Which language is used in the Task when input data structure is text.|
+|Positive class| Required when Task Type is Binary Classification, it indicates the positive class to be predicted.|
+|Context separator| Available when Task Type is RAG, it specifies the string separator to split retrieved context into different chunks. |
 
 !!! warning
     Some Task's attributes are immutable: type, data structure and optional target flag cannot be modified after the creation of the Task.
 
+## Platform modules and Task Type compatibility
+
 Most of ML cube Platform operations are done at Task level: monitoring, retraining, analytics and other features are specific to AI models and data that belong to a Task.
-Indeed, each task type has a set of ML cube Platform modules:
+Indeed, each Task Type has a set of ML cube Platform modules:
 
 | Module | Regression | Classification | RAG | Object Detection |
 | --------- | --------- | --------- | --------- | --------- |
@@ -46,21 +50,21 @@ Indeed, each task type has a set of ML cube Platform modules:
 | LLM Security | :material-close: | :material-close: | :material-check: | :material-close: |
 
 !!! Tip
-    On the left side of the web app page is present the Task menù with links to the above mentioned modules and Task settings.
+    On the left side of the web app page the Task menù is present, with links to the above mentioned modules and Task settings.
 
 ## Task Type
 
-ML cube Platform supports several task types providing specific features for each of them.
-Not all task types are compatible with data structures, in the table below are shown which data structure is supported by which task type:
+ML cube Platform supports several Task Types providing specific features for each of them.
+Not all Task Types are compatible with data structures, in the table below are shown which data structure is supported by which Task Type:
 
-|Task type| Tabular | Image | Text | Embedding|
+|Task Type| Tabular | Image | Text | Embedding|
 | -- | -- | -- | -- | -- |
 | Regression | :material-check: | :material-check: | :material-check: | :material-check: |
 | Classification | :material-check: | :material-check: | :material-check: | :material-check: |
 | RAG | :material-close: | :material-close: | :material-check: | :material-check: |
 | Object Detection | :material-close: | :material-check: | :material-close: | :material-check: |
 
-In the following sections, you can find a description of each task type with its specific information.
+In the following sections, you can find a description of each Task Type with its specific information.
 
 ### Regression
 
@@ -103,7 +107,7 @@ $$
 where $N_{FP}$ and $N_{FN}$ are the number of false positives and false negatives respectively.
 
 - **Multiclass:**
-    - $c_{k}$ is the cost of misclassifying a sample which actual class is $k$ with another class
+    - $c_{k}$ is the cost of misclassifying a sample, whose actual class is $k$, with another class
 
     Given a data batch, the mean cost $\bar{C}$ is expressed as 
 $$
@@ -125,20 +129,20 @@ where $N_{FP}^{k}$ and $N_{FN}^{k}$ are the number of false positives and false
 
 ### Retrieval Augmented Generation
 
-Retrieval Augmented Generation is a particular AI task for Text data based on Large Language Models to generate responses of user query using a set of retrieved documents as context to generate a precise and more focused response.
+Retrieval Augmented Generation is a particular AI task for Text data based on Large Language Models, in which they are used to generate responses of user query using a set of retrieved documents as context to provide a precise and more focused response.
 
 RAG Tasks, do not have a Target therefore, the attribute *optional target* is always set to True.
-Moreover, in this Task, the Target is a text as well and the input is composed of two entities:
+Moreover, in this Task, the Prediction is a text as well and the input is composed of two entities:
 
 - User Input: the user query that the model needs to answer
 - Retrieved Context: the set of documents the retrieval engine selected to help the model
 
-RAG tasks has additional attribute *context separator* which is string used to separate different retrieved contexts into chunks. Context data is sent as a single string, however, in RAG settings multiple documents can be retrieved. In this case, context separator is used to distinguish them. It is optional since a single context can be provided.
+RAG tasks has additional the attribute *context separator* which is a string used to separate different retrieved contexts into chunks. Context data is sent as a single string, however, in RAG settings multiple documents can be retrieved. In this case, context separator is used to distinguish them. It is optional since a single context can be provided.
 
 !!! example
-    Context separator: <CONTEXT>
+    Context separator: <<sep>>
 
-    Context data: The capital of Italy is Rome.<CONTEXT>Rome is the capital of Italy.<CONTEXT>Rome was the capital of Roman Empire.
+    Context data: The capital of Italy is Rome.<<sep>>Rome is the capital of Italy.<<sep>>Rome was the capital of Roman Empire.
 
     Contexts: