QueryClassifier can categorize queries into keyword-based and natural language queries. A common use case for QueryClassifier is in a question answering pipeline where it routes keyword queries to a less computationally expensive keyword-based Retriever and natural language questions to a vector-based Retriever. This helps you save time and can produce better results for your keyword queries.

To handle these tasks, QueryClassifier uses a classification model.

Basic Information

Pipeline type: Used in query pipelines.
Position in a pipeline: At the beginning.
Node input: Query
Node output: Query
Available node classes: TransformersQueryClassifier

When used in a pipeline, QueryClassifier acts as a decision node, which means it routes the queries to a specific node, depending on how the query is classified.

Overview

TransformersQueryClassifier is sensitive to the syntax of a sentence as it uses a transformer model to classify queries. The default model is shahrukhx01/bert-mini-finetune-question-detection. It was trained using the mini BERT architecture of about 50 MB in size, which allows relatively fast inference on the CPU. It supports zero-shot classification.

Usage Examples

...
components:
	- name: QueryClassifier
    type: TransformersQueryClassifier
    params:
    	model_name_or_path: "shahrukhx01/bert-mini-finetune-question-detection"
...
pipelines:
 - name: query
   nodes:
      - name: QueryClassifier
        inputs: [Query]
      - name: KeywordRetriever # such as BM25Retriever
        inputs: [QueryClassifier.output_1] # This output edge routes keyword queries further down the pipeline
      - name: VectorRetriever # such as DensePassageRetriever
        inputs: [QueryClassifier.output_2] # This output edge routes natural language queries further down the pipeline
   ...

Arguments

Argument	Type	Possible Values	Description
`model_name_or_path`	String	Default: `shahrukhx01/bert-mini-finetune-question-detection`	Specifies the model you want to use. You can either type a path to the model stored on your computer or the name of a public model from Hugging Face. The default model was trained on the mini BERT architecture and can distinguish between natural language queries and questions. Mandatory.
`model_version`	String	Tag name Branch name Commit hash	The version of the model from Hugging Face. Optional.
`tokenizer`	String	Default: `None`	The name of the tokenizer, usually the same as the model name. Optional.
`use_gpu`	Boolean	`True` (default) `False`	Specifies if GPU should be used. Mandatory.
`task`	String	`text-classification` (default) `zero-shot-classification`	Specifies the type of classification the node should perform. `text-classification` - Choose this task if you have a model trained with a defined list of labels. `zero-shot-classification` - Choose if you want to define labels at runtime. Mandatory.
`labels`	A list of strings	Default: `None`	If you choose `text-classification` as `task` and provide an ordered label, the first label corresponds to output_1, the second label corresponds to output_2, and so on. The labels must match the model labels; only their order can differ. If you selected `zero-shot-classification` as `task`, these are the candidate labels. Mandatory.
`batch_size`	Integer	Default: `16`	The number of queries you want to process at one time. Mandatory.
`progress_bar`	Boolean	`True` (default) `False`	Shows the progress bar when processing queries. Mandatory.
`use_auth_token`	String or Boolean	Default: `None`	Specifies the API token used to download private models from Hugging Face. If you set it to `True`, it uses the token generated when running `transformers-cli login`. Optional.
`devices`	String or torch.device	Default: `None`	A list of torch devices such as cuda, cpu, mps, to limit inference to specific devices. Example: `[torch.device(`cuda:0`), "mps, "cuda:1"` If you set `use_gpu` to `False`, this parameter is not used and a single cpu device is used for inference. Optional.