ValueError when using prepare_for_training() on multi-label annotated Argilla dataset with single annotations
**Describe the bug**
I'm getting a `ValueError` when trying to use the `prepare_for_training()` method to prepare a multi-label annotated Argilla dataset for training. I'm only getting the error when a given record has just a single annotation. Everything works fine if all records have more than one assigned annotation label.

**To Reproduce**
```python
import datetime
import argilla as rg

# Create dataset with a multi-label record that only has one annotation
dataset_rg = rg.DatasetForTextClassification(
    [
        rg.TextClassificationRecord(text=None,
                                    inputs={'title': 'This is the title of an article'}, 
                                    prediction=None, 
                                    prediction_agent=None, 
                                    annotation=['LABEL_1'], 
                                    annotation_agent='team', 
                                    vectors=None, 
                                    multi_label=True, 
                                    explanation=None, 
                                    id='12345', 
                                    metadata={'split': 'train'}, 
                                    status='Validated', 
                                    event_timestamp=datetime.datetime(2023, 4, 4, 9, 57, 59, 910986), 
                                    search_keywords=None)
    ]
)

# Prepare the dataset for training
dataset_rg.prepare_for_training(framework="setfit")
```
Generates the following error:
```python
ValueError: Class label 1 greater than configured num_classes 1
```

**Expected behavior**
Expect the method to return a `Dataset` that is ready for training.
```python
Dataset({
    features: ['id', 'text', 'label', 'binarized_label'],
    num_rows: 1
})
```

**Environment (please complete the following information):**
 - OS [e.g. iOS]: GitHub Codespaces - Ubuntu Linux
 - Argilla Version [e.g. 1.0.0]: 1.6.0

**Additional context**
The method works properly when the given record is multi-label AND has more than one annotation label, for example:
```python
import datetime
import argilla as rg

# Create dataset with a multi-label record that only has one annotation
dataset_rg = rg.DatasetForTextClassification(
    [
        rg.TextClassificationRecord(text=None,
                                    inputs={'title': 'This is the title of an article'}, 
                                    prediction=None, 
                                    prediction_agent=None, 
                                    annotation=['LABEL_1', 'LABEL_2'], 
                                    annotation_agent='team', 
                                    vectors=None, 
                                    multi_label=True, 
                                    explanation=None, 
                                    id='12345', 
                                    metadata={'split': 'train'}, 
                                    status='Validated', 
                                    event_timestamp=datetime.datetime(2023, 4, 4, 9, 57, 59, 910986), 
                                    search_keywords=None)
    ]
)

# Prepare the dataset for training
dataset_rg.prepare_for_training(framework="setfit")
```
Returns:
```python
Dataset({
    features: ['id', 'text', 'label', 'binarized_label'],
    num_rows: 1
})
```

diff --git a/src/argilla/client/datasets.py b/src/argilla/client/datasets.py
index 0295b54f..59cae9c6 100644
--- a/src/argilla/client/datasets.py
+++ b/src/argilla/client/datasets.py
@@ -741,6 +741,11 @@ class DatasetForTextClassification(DatasetBase):
         else:
             labels = {label: None for label in ds_dict["label"]}
 
+        if len(labels) == 1:
+            raise ValueError(
+                "The dataset contains only one labels. Classification datasets should have at least 2 labels."
+            )
+
         class_label = (
             datasets.ClassLabel(names=sorted(labels.keys()))
             if ds_dict["label"]
['src/argilla/client/datasets.py']