Add classifier page

docs/latest/components/classifier.mdx

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+# Classifier
+
+The Classifier Node is a transformer based classification model used to create predictions that can be attached to retrieved documents as metadata.
+For example, by using a sentiment model, you can label each document as being either positive or negative in sentiment.
+Through a tight integration with the HuggingFace model hub, you can easily load any classification model by simply supplying the model name.
+
+![image](/img/classifier.png)
+
+<div className="max-w-xl bg-yellow-light-theme border-l-8 border-yellow-dark-theme px-6 pt-6 pb-4 my-4 rounded-md dark:bg-yellow-900">
+
+Note that the Classifier is different from the Query Classifier.
+While the Query Classifier categorizes incoming queries in order to route them to different parts of the pipeline,
+the Classifier is used to create classification labels that can be attached to retrieved documents as metadata.
+
+</div>
+
+## Usage
+
+Initialize it as follows:
+
+``` python
+from haystack.classifier import FARMClassifier
+
+classifier_model = 'textattack/bert-base-uncased-imdb'
+classifier = FARMClassifier(model_name_or_path=classifier_model)
+```
+
+It slotted into a pipeline as follows:
+
+``` python
+pipeline = Pipeline()
+pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
+pipeline.add_node(component=classifier, name='Classifier', inputs=['Retriever'])
+```
+
+It can also be run in isolation:
+
+``` python
+documents = classifier.predict(
+ query="",
+ documents = [doc1, doc2, doc3, ...]
+):
+```