More improvements

chapters/en/chapter10/2.mdx

@@ -34,12 +34,12 @@ Let's connect with our Argilla instance. To do that you will need the following
 ```python
 import argilla as rg
 
-HF_TOKEN = "..." # only for private spaces
+HF_TOKEN = "..."  # only for private spaces
 
 client = rg.Argilla(
     api_url="...",
     api_key="...",
-    headers={"Authorization": f"Bearer {HF_TOKEN}"} # only for private spaces
+    headers={"Authorization": f"Bearer {HF_TOKEN}"},  # only for private spaces
 )
 ```
 

chapters/en/chapter10/3.mdx

@@ -18,12 +18,12 @@ The first step is to connect to our Argilla instance as we did in the previous s
 ```python
 import argilla as rg
 
-HF_TOKEN = "..." # only for private spaces
+HF_TOKEN = "..."  # only for private spaces
 
 client = rg.Argilla(
     api_url="...",
     api_key="...",
-    headers={"Authorization": f"Bearer {HF_TOKEN}"} # only for private spaces
+    headers={"Authorization": f"Bearer {HF_TOKEN}"},  # only for private spaces
 )
 ```
 
@@ -46,22 +46,18 @@ It contains a `text` and also some initial labels for the text classification. W
 
 ```python
 settings = rg.Settings(
-    fields=[
-        rg.TextField(name="text")
-    ],
+    fields=[rg.TextField(name="text")],
     questions=[
         rg.LabelQuestion(
-            name="label", 
-            title="Classify the text:", 
-            labels=data.unique("label_text")
+            name="label", title="Classify the text:", labels=data.unique("label_text")
         ),
         rg.SpanQuestion(
-            name="entities", 
-            title="Highlight all the entities in the text:", 
-            labels=["PERSON", "ORG", "LOC", "EVENT"], 
-            field="text"
-        )
-    ]
+            name="entities",
+            title="Highlight all the entities in the text:",
+            labels=["PERSON", "ORG", "LOC", "EVENT"],
+            field="text",
+        ),
+    ],
 )
 ```
 
@@ -79,10 +75,7 @@ To learn more about all the available types of fields and questions and other ad
 Now that we've defined some settings, we can create the dataset:
 
 ```python
-dataset = rg.Dataset(
-    name="ag_news",
-    settings=settings
-)
+dataset = rg.Dataset(name="ag_news", settings=settings)
 
 dataset.create()
 ```

chapters/en/chapter10/4.mdx

@@ -4,7 +4,7 @@
 
 ##TODO: Add screenshots!
 
-Now it is time to start working from the Argilla UI to annotate your dataset.
+Now it is time to start working from the Argilla UI to annotate our dataset. 
 
 ## Align your team with annotation guidelines
 
@@ -20,9 +20,8 @@ Sometimes, you want to have more than one submitted response per record, for exa
 
 ## Annotate records
 
-<Tip>
-💡 If you are deploying Argilla in a Hugging Face Space, any team members will be able to log in using the Hugging Face OAuth. Otherwise, you may need to create users for them following [this guide](https://docs.argilla.io/latest/how_to_guides/user/).
-</Tip>
+>[!TIP]
+>💡 If you are deploying Argilla in a Hugging Face Space, any team members will be able to log in using the Hugging Face OAuth. Otherwise, you may need to create users for them following [this guide](https://docs.argilla.io/latest/how_to_guides/user/).
 
 When you open your dataset, you will realize that the first question is already filled in with some suggested labels. That's because in the previous section we mapped our question called `label` to the `label_text` column in the dataset, so that we simply need to review and correct the already existing labels. For the token classification, we'll need to add all labels manually, as we didn't include any suggestions.
 
@@ -34,7 +33,7 @@ As you move through the different records, there are different actions you can t
 In the next section, you will learn how you can export and use those annotations.
 
 ---
-
+Examples of images from other chapters:
 <a class="flex justify-center" href="/huggingface-course/bert-finetuned-ner">
 <img class="block dark:hidden lg:w-3/5" src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter7/model-eval-bert-finetuned-ner.png" alt="One-hot encoded labels for question answering."/>
 <img class="hidden dark:block lg:w-3/5" src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter7/model-eval-bert-finetuned-ner-dark.png" alt="One-hot encoded labels for question answering."/>

chapters/en/chapter10/5.mdx

@@ -9,12 +9,12 @@ First, we'll need to make sure that we're connected to our Argilla instance as i
 ```python
 import argilla as rg
 
-HF_TOKEN = "..." # only for private spaces
+HF_TOKEN = "..."  # only for private spaces
 
 client = rg.Argilla(
     api_url="...",
     api_key="...",
-    headers={"Authorization": f"Bearer {HF_TOKEN}"} # only for private spaces
+    headers={"Authorization": f"Bearer {HF_TOKEN}"},  # only for private spaces
 )
 ```
 
@@ -31,26 +31,20 @@ Loading the dataset and calling its records with `dataset.records` is enough to
 Sometimes you only want to use the records that have been completed, so we will first filter the records in our dataset based on their status:
 
 ```python
-status_filter = rg.Query(
-    filter=rg.Filter(
-        [
-            ("status", "==", "completed")
-        ]
-    )
-)
+status_filter = rg.Query(filter=rg.Filter([("status", "==", "completed")]))
 
 filtered_records = dataset.records(status_filter)
 ```
 
-<Tip>
-⚠️ Note that the records could have more than one response and that each of them can have any status from `submitted`, `draft` or `discarded`.
-</Tip>
+>[!TIP]
+>⚠️ Note that the records could have more than one response and that each of them can have any status from `submitted`, `draft` or `discarded`.
+
 
 Learn more about querying and filtering records in the [Argilla docs](https://docs.argilla.io/latest/how_to_guides/query/).
 
 ## Export to the Hub
 
-We can now export our records to a Dataset in the Hugging Face Hub, so we can share our annotations with others. To do this, we'll need to convert the records into a Dataset and then push it to the Hub:
+We can now export our annotations to the Hugging Face Hub, so we can share them with others. To do this, we'll need to convert the records into a 🤗 Dataset and then push it to the Hub:
 
 ```python
 filtered_records.to_datasets().push_to_hub("argilla/ag_news_annotated")