Fix some grammar errors and redundant words

docs/docs/action-server/events.mdx

@@ -87,7 +87,7 @@ Schedules an intent to be triggered at a certain time in the future.
 
 **Parameters**:
 
-* `intent`: Intent which the reminder will  trigger 
+* `intent`: Intent which the reminder will trigger 
 * `entities`: Entities to send with the intent
 * `date_time`: Date at which the execution of the action should be triggered. This should either be in UTC or include a timezone.
 * `name`: ID of the reminder. If there are multiple reminders with the same id only the last will be run.

docs/docs/action-server/sdk-events.mdx

@@ -88,7 +88,7 @@ rasa_sdk.events.ReminderScheduled(
 
 **Parameters**:
 
-* `intent_name`: Intent which the reminder will  trigger 
+* `intent_name`: Intent which the reminder will trigger 
 * `trigger_date_time`: Datetime at which the execution of the action should be triggered. 
 * `entities`: Entities to send with the intent
 * `name`: ID of the reminder. If there are multiple reminders with the same id only the last will be run.

docs/docs/connectors/hangouts.mdx

@@ -13,7 +13,7 @@ more information, see the Cards section).
 
 In order to connect your Rasa bot to Google Hangouts Chat, you first need to create a project in
 Google Developer Console that includes the Hangouts API. There you can specify your bot's endpoint.
-This endpoint should look like look like `https://<host>:<port>/webhooks/hangouts/webhook`, replacing
+This endpoint should look like `https://<host>:<port>/webhooks/hangouts/webhook`, replacing
 the host and port with the appropriate values from your running Rasa server.
 
 :::note configure https

docs/docs/custom-graph-components.mdx

@@ -384,7 +384,7 @@ to specify the following details:
       inference.
       Rasa will use the graph component's `train` method if
       `is_trainable=True` is specified. Rasa will use
-      the graph scomponent's `process` method to extract entities during inference.
+      the graph component's `process` method to extract entities during inference.
 
    * `ComponentType.POLICY_WITHOUT_END_TO_END_SUPPORT`: Component type for
       [policies](policies.mdx) which don't require additional end-to-end features

docs/docs/deploy/deploy-rasa.mdx

@@ -143,7 +143,7 @@ Visit [the Rasa Helm Chart repository](https://github.com/RasaHQ/helm-charts/tre
 
 ### 3. Loading an initial model
 
-The first time you install Rasa, you may not have a model server available yet, or you may want an lightweight model for testing the deployment.
+The first time you install Rasa, you may not have a model server available yet, or you may want a lightweight model for testing the deployment.
 For this purpose, you can choose between training or downloading an initial model. By default, the Rasa chart downloads an example model from GitHub.
 To use this option, you don't have to change anything.
 

docs/docs/graph-recipe.mdx

@@ -107,7 +107,7 @@ avoid slow first prediction).
 eager: true
 ```
 
-- `resource`: If given, graph node is loaded from this resource instead of of instantiated from scratch. This is e.g. used to load a trained component for predictions.
+- `resource`: If given, graph node is loaded from this resource instead of instantiated from scratch. This is e.g. used to load a trained component for predictions.
 
 ```yaml-rasa
 resource:

docs/docs/model-configuration.mdx

@@ -12,7 +12,7 @@ Currently, "default.v1" and the experimental "graph.v1" recipes are supported.
 The language and pipeline keys specify the [components](./components.mdx) used by the  model to make NLU predictions.
 The policies key defines the [policies](./policies.mdx) used by the model to predict the next action.
 
-If you don't know which components or policies to choose, you can use the
+If you don't know which components or policies to choose, you can use
 the [Suggested Config](./model-configuration.mdx#suggested-config) feature, which will recommend sensible defaults.
 
 ## Suggested Config

docs/docs/monitoring/analytics/example-queries.mdx

@@ -27,7 +27,7 @@ Metabase queries because we used filters in Metabase to remove the noise and mak
 the graphs look nice.
 
 Please make sure that these SQL queries are kept in sync with the dashboard, it'll
-make your colleagues' work next time easier :)
+make your colleagues' work next time easier :).
 
 -->
 

docs/docs/nlu-only.mdx

@@ -31,7 +31,7 @@ rasa shell nlu
 This will start the rasa shell and ask you to type in a message to test.
 You can keep typing in as many messages as you like.
 
-Alternatively, you can leave out the ``nlu`` argument and pass in an nlu-only model directly:
+Alternatively, you can leave out the ``nlu`` argument and pass in a nlu-only model directly:
 
 ```bash
 rasa shell -m models/nlu-20190515-144445.tar.gz

docs/docs/rules.mdx

@@ -45,7 +45,7 @@ rules:
 ```
 
 This example rule applies at the start of conversation as well as when the user decides
-to a send a message with an intent `greet` in the middle of an ongoing conversation.
+to send a message with an intent `greet` in the middle of an ongoing conversation.
 
 Dialogue turns that only appear as rules in the training data and do not appear in stories
 will be ignored by ML-only policies like `TEDPolicy` at prediction time.

docs/docs/training-data-format.mdx

@@ -312,7 +312,7 @@ nlu:
     - \d{10,12}
 ```
 
-Here `account_number` is the name of the regular expression. When used as as features for the `RegexFeaturizer` the name of the regular expression does not matter. When using the `RegexEntityExtractor`, the name of the regular expression should match the name of the entity you want to extract.
+Here `account_number` is the name of the regular expression. When used as features for the `RegexFeaturizer` the name of the regular expression does not matter. When using the `RegexEntityExtractor`, the name of the regular expression should match the name of the entity you want to extract.
 
 
 Read more about when and how to use regular expressions with each component on the [NLU Training Data page](./nlu-training-data.mdx#regular-expressions).
@@ -491,7 +491,7 @@ stories:
 The `action` step activates the form and begins looping over the required slots. The `active_loop: restaurant_form`
 step indicates that there is a currently active form. Much like a `slot_was_set` step,
 a `form` step doesn't **set** a form to active but indicates that it should already be activated.
-In the same way, the  `active_loop: null` step indicates that no form should be active before the subsequent
+In the same way, the `active_loop: null` step indicates that no form should be active before the subsequent
 steps are taken.
 
 A form can be interrupted and remain active; in this case the interruption should come after the