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Module base


class BaseDocumentClassifier(BaseComponent)


def timing(fn, attr_name)

Wrapper method used to time functions.

Module transformers


class TransformersDocumentClassifier(BaseDocumentClassifier)

Transformer based model for document classification using the HuggingFace's transformers framework
While the underlying model can vary (BERT, Roberta, DistilBERT ...), the interface remains the same.
This node classifies documents and adds the output from the classification step to the document's meta data.
The meta field of the document is a dictionary with the following format:
'meta': {'name': '450_Baelor.txt', 'classification': {'label': 'neutral', 'probability' = 0.9997646, ...} }

Classification is run on document's content field by default. If you want it to run on another field,
set the classification_field to one of document's meta fields.

With this document_classifier, you can directly get predictions via predict()

Usage example at query time:

|    ...
|    retriever = BM25Retriever(document_store=document_store)
|    document_classifier = TransformersDocumentClassifier(model_name_or_path="bhadresh-savani/distilbert-base-uncased-emotion")
|    p = Pipeline()
|    p.add_node(component=retriever, name="Retriever", inputs=["Query"])
|    p.add_node(component=document_classifier, name="Classifier", inputs=["Retriever"])
|    res =
|        query="Who is the father of Arya Stark?",
|        params={"Retriever": {"top_k": 10}}
|    )
|    # print the classification results
|    print_documents(res, max_text_len=100, print_meta=True)
|    # or access the predicted class label directly
|    res["documents"][0].to_dict()["meta"]["classification"]["label"]

Usage example at index time:

|    ...
|    converter = TextConverter()
|    preprocessor = Preprocessor()
|    document_store = ElasticsearchDocumentStore()
|    document_classifier = TransformersDocumentClassifier(model_name_or_path="bhadresh-savani/distilbert-base-uncased-emotion",
|                                                         batch_size=16)
|    p = Pipeline()
|    p.add_node(component=converter, name="TextConverter", inputs=["File"])
|    p.add_node(component=preprocessor, name="Preprocessor", inputs=["TextConverter"])
|    p.add_node(component=document_classifier, name="DocumentClassifier", inputs=["Preprocessor"])
|    p.add_node(component=document_store, name="DocumentStore", inputs=["DocumentClassifier"])


def __init__(model_name_or_path: str = "bhadresh-savani/distilbert-base-uncased-emotion", model_version: Optional[str] = None, tokenizer: Optional[str] = None, use_gpu: bool = True, return_all_scores: bool = False, task: str = "text-classification", labels: Optional[List[str]] = None, batch_size: int = -1, classification_field: str = None)

Load a text classification model from Transformers.

Available models for the task of text-classification include:

  • 'bhadresh-savani/distilbert-base-uncased-emotion'
  • 'Hate-speech-CNERG/dehatebert-mono-english'

Available models for the task of zero-shot-classification include:

  • 'valhalla/distilbart-mnli-12-3'
  • 'cross-encoder/nli-distilroberta-base'

See for full list of available models.
Filter for text classification models:
Filter for zero-shot classification models (NLI):


  • model_name_or_path: Directory of a saved model or the name of a public model e.g. 'bhadresh-savani/distilbert-base-uncased-emotion'.
    See for full list of available models.
  • model_version: The version of model to use from the HuggingFace model hub. Can be tag name, branch name, or commit hash.
  • tokenizer: Name of the tokenizer (usually the same as model)
  • use_gpu: Whether to use GPU (if available).
  • return_all_scores: Whether to return all prediction scores or just the one of the predicted class. Only used for task 'text-classification'.
  • task: 'text-classification' or 'zero-shot-classification'
  • labels: Only used for task 'zero-shot-classification'. List of string defining class labels, e.g.,
    ["positive", "negative"] otherwise None. Given a LABEL, the sequence fed to the model is " sequence to
    classify This example is LABEL . " and the model predicts whether that sequence is a contradiction
    or an entailment.
  • batch_size: batch size to be processed at once
  • classification_field: Name of Document's meta field to be used for classification. If left unset, Document.content is used by default.


def predict(documents: List[Document]) -> List[Document]

Returns documents containing classification result in meta field.

Documents are updated in place.


  • documents: List of Document to classify


List of Document enriched with meta information