docs: extend tutorial14 about query classification

docs/_src/tutorials/tutorials/14.md

@@ -56,7 +56,7 @@ Next we make sure the latest version of Haystack is installed:
 !pip install pygraphviz
 ```
 
-## Logging
+### Logging
 
 We configure how logging messages should be displayed and which log level should be used before importing Haystack.
 Example log message:
@@ -339,6 +339,99 @@ print(f"\n\n{equal_line}\nSTATEMENT QUERY RESULTS\n{equal_line}")
 print_documents(res_2)
 ```
 
+### Other use cases for Query Classifiers: custom classification models and zero shot classification
+
+`TransformersQueryClassifier` is very flexible and also supports other possibilities for classifying queries, including loading a custom classification model from Transformers Hub or using zero shot classification.
+
+#### Using custom classification models
+We can use a public model, available in the Transformer Hub. For example, we might be interested in classifying the sentiment of the *queries*, so we choose a appropriate model, such as https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment.### Using zero-shot-classification
+
+
+```python
+from haystack.nodes import TransformersQueryClassifier
+
+# remember to compile a list with the exact model labels
+# the first provided label will corresponds to output_1, the second label to output_2, and so on.
+labels = ["LABEL_0", "LABEL_1", "LABEL_2"]
+
+sentiment_query_classifier = TransformersQueryClassifier(
+    model_name_or_path="cardiffnlp/twitter-roberta-base-sentiment",
+    use_gpu=True,
+    task="text-classification",
+    labels=labels,
+)
+```
+
+
+```python
+queries = [
+    "What's the answer?",  # neutral query
+    "Would you be so lovely to tell me the answer?",  # positive query
+    "Can you give me the damn right answer for once??",  # negative query
+]
+```
+
+
+```python
+import pandas as pd
+
+sent_results = {"Query": [], "Output Branch": [], "Class": []}
+
+for query in queries:
+    result = sentiment_query_classifier.run(query=query)
+    sent_results["Query"].append(query)
+    sent_results["Output Branch"].append(result[1])
+    if result[1] == "output_1":
+        sent_results["Class"].append("negative")
+    elif result[1] == "output_2":
+        sent_results["Class"].append("neutral")
+    elif result[1] == "output_3":
+        sent_results["Class"].append("positive")
+
+pd.DataFrame.from_dict(sent_results)
+```
+
+#### Using zero shot classification
+It is also possible to perform zero shot classifcation, by providing a suitable base transformer model and **choosing** the classes that the model should predict.
+For example, we may be interested in whether the user query is related to music or cinema.
+
+
+```python
+# in zero-shot-classification, the labels can be freely chosen
+labels = ["music", "cinema"]
+
+query_classifier = TransformersQueryClassifier(
+    model_name_or_path="typeform/distilbert-base-uncased-mnli",
+    use_gpu=True,
+    task="zero-shot-classification",
+    labels=labels,
+)
+```
+
+
+```python
+queries = [
+    "In which films does John Travolta appear?",  # query about cinema
+    "What is the Rolling Stones first album?",  # query about music
+    "Who was Sergio Leone?",  # query about cinema
+]
+```
+
+
+```python
+import pandas as pd
+
+query_classification_results = {"Query": [], "Output Branch": [], "Class": []}
+
+for query in queries:
+    result = query_classifier.run(query=query)
+    query_classification_results["Query"].append(query)
+    query_classification_results["Output Branch"].append(result[1])
+    query_classification_results["Class"].append("music" if result[1] == "output_1" else "cinema")
+
+pd.DataFrame.from_dict(query_classification_results)
+```
+
 ## About us
 
 This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany