TransformersZeroShotDocumentClassifier

Classify documents based on the labels you provide and add the predicted label to the document's metadata.

Key Features

• Classifies documents using a zero-shot Hugging Face NLI model — no training data required.
• Adds a classification metadata field to each document with the predicted label.
• Supports multi-label classification to allow multiple labels per document.
• Runs classification on Document.content by default, or on a custom metadata field.
• Compatible with MetadataRouter for routing documents based on their classification.

Configuration

1. Drag the TransformersZeroShotDocumentClassifier component onto the canvas from the Component Library.
2. Click the component to open the configuration panel.
3. On the General tab:
   1. Enter the model name or path, such as cross-encoder/nli-deberta-v3-xsmall.
   2. Enter the labels to use for classification, such as ["positive", "negative"].
4. Go to the Advanced tab to configure batch size, multi-label mode, device, token, and Hugging Face pipeline kwargs.

Connections

TransformersZeroShotDocumentClassifier receives documents — typically from a converter like TextFileToDocument. It outputs the same documents list with an added classification metadata field. Connect its output to MetadataRouter or DocumentWriter.

Usage Example

In this index, TransformersZeroShotDocumentClassifier classifies documents by sentiment (positive or negative) and sends classified documents to MetadataRouter. MetadataRouter then routes positive documents to one document store and negative documents to another.

components:
  TextFileToDocument:
    type: haystack.components.converters.txt.TextFileToDocument
    init_parameters:
      encoding: utf-8
      store_full_path: false
  TransformersZeroShotDocumentClassifier:
    type: haystack_integrations.classifiers.zero_shot_document_classifier.TransformersZeroShotDocumentClassifier
    init_parameters:
      model: cross-encoder/nli-deberta-v3-xsmall
      labels:
      - positive
      - negative
      multi_label: false
      classification_field:
      device:
      token:
        type: env_var
        env_vars:
        - HF_API_TOKEN
        - HF_TOKEN
        strict: false
      huggingface_pipeline_kwargs:
  MetadataRouter:
    type: haystack.components.routers.metadata_router.MetadataRouter
    init_parameters:
      rules:
        positive:
          operator: OR
          conditions:
          - field: classification.label
            operator: ==
            value: positive
        negative:
          operator: OR
          conditions:
          - field: classification.label
            operator: ==
            value: negative
  DocumentWriter_Positive:
    type: haystack.components.writers.document_writer.DocumentWriter
    init_parameters:
      policy: NONE
      document_store:
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          hosts:
          index: 'positive-sentiment-index'
          max_chunk_bytes: 104857600
          embedding_dim: 768
          return_embedding: false
          method:
          mappings:
          settings:
          create_index: true
          http_auth:
          use_ssl:
          verify_certs:
          timeout:
  DocumentWriter_Negative:
    type: haystack.components.writers.document_writer.DocumentWriter
    init_parameters:
      policy: NONE
      document_store:
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          hosts:
          index: 'negative-sentiment-index'
          max_chunk_bytes: 104857600
          embedding_dim: 768
          return_embedding: false
          method:
          mappings:
          settings:
          create_index: true
          http_auth:
          use_ssl:
          verify_certs:
          timeout:

connections:  # Defines how the components are connected
- sender: TextFileToDocument.documents
  receiver: TransformersZeroShotDocumentClassifier.documents
- sender: TransformersZeroShotDocumentClassifier.documents
  receiver: MetadataRouter.documents
- sender: MetadataRouter.positive
  receiver: DocumentWriter_Positive.documents
- sender: MetadataRouter.negative
  receiver: DocumentWriter_Negative.documents

inputs:  # Define the inputs for your pipeline
  files:
  - TextFileToDocument.sources

max_runs_per_component: 100

metadata: {}

Parameters

Inputs

Parameter	Type	Default	Description
documents	List[Document]		Documents to process.
batch_size	int (Optional)	1	Batch size used for processing the content in each document.

Outputs

Parameter	Type	Default	Description
documents	List[Document]		A list of documents with an added metadata field called classification.

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
model	str		The name or path of a Hugging Face model for zero shot document classification.
labels	List[str]		The set of possible class labels to classify each document into, for example, ["positive", "negative"]. The labels depend on the selected model.
multi_label	bool	False	Whether or not multiple candidate labels can be true. If False, the scores are normalized such that the sum of the label likelihoods for each sequence is 1. If True, the labels are considered independent and probabilities are normalized for each candidate by doing a softmax of the entailment score vs. the contradiction score.
classification_field	Optional[str]	None	Name of document's meta field to be used for classification. If not set, Document.content is used by default.
device	Optional[ComponentDevice]	None	The device on which the model is loaded. If None, the default device is automatically selected. If a device/device map is specified in huggingface_pipeline_kwargs, it overrides this parameter.
token	Optional[Secret]	Secret.from_env_var(['HF_API_TOKEN', 'HF_TOKEN'], strict=False)	The Hugging Face token to use as HTTP bearer authorization. Check your HF token in your account settings.
huggingface_pipeline_kwargs	Optional[Dict[str, Any]]	None	Dictionary containing keyword arguments used to initialize the Hugging Face pipeline for text classification.

Run Method Parameters

These are the parameters you can configure for the component's run() method. This means you can pass these parameters at query time through the API, in Playground, or when running a job. For details, see Modify Pipeline Parameters at Query Time.

Parameter	Type	Default	Description
documents	List[Document]		Documents to process.
batch_size	int	1	Batch size used for processing the content in each document.