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Used to create predictions that are attached to documents as metadata.

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': 'love', 'score': 0.960899, 'details': {'love': 0.960899, 'joy': 0.032584, ...}}}

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

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",
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,
             top_k: Optional[int] = 1,
             task: str = "text-classification",
             labels: Optional[List[str]] = None,
             batch_size: int = 16,
             classification_field: Optional[str] = None,
             progress_bar: bool = True,
             use_auth_token: Optional[Union[str, bool]] = None,
             devices: Optional[List[Union[str, "torch.device"]]] = 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).
  • top_k: The number of top predictions to return. The default is 1. Enter None to return all the predictions. 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: Number of Documents to be processed at a time.
  • classification_field: Name of Document's meta field to be used for classification. If left unset, Document.content is used by default.
  • progress_bar: Whether to show a progress bar while processing.
  • use_auth_token: The API token used to download private models from Huggingface. If this parameter is set to True, then the token generated when running transformers-cli login (stored in ~/.huggingface) will be used. Additional information can be found here
  • devices: List of torch devices (e.g. cuda, cpu, mps) to limit inference to specific devices. A list containing torch device objects and/or strings is supported (For example [torch.device('cuda:0'), "mps", "cuda:1"]). When specifying use_gpu=False the devices parameter is not used and a single cpu device is used for inference.


def predict(documents: List[Document],
            batch_size: Optional[int] = None) -> List[Document]

Returns documents containing classification result in a meta field.

Documents are updated in place.


  • documents: A list of Documents to classify.
  • batch_size: The number of Documents to classify at a time.


A list of Documents enriched with meta information.


def predict_batch(
    documents: Union[List[Document], List[List[Document]]],
    batch_size: Optional[int] = None
) -> Union[List[Document], List[List[Document]]]

Returns documents containing classification result in meta field.

Documents are updated in place.


  • documents: List of Documents or list of lists of Documents to classify.
  • batch_size: Number of Documents to classify at a time.


List of Documents or list of lists of Documents enriched with meta information.

def run(documents: Union[List[dict], List[Document]], root_node: str)


  • documents: A list of Document objects.
  • root_node: The root node of the pipeline's graph.


def run_batch(documents: Union[List[Document], List[List[Document]]],
              batch_size: Optional[int] = None)


  • documents: List of list of Documents.
  • batch_size: Number of Documents to process at a time.


def timing(fn, attr_name)

Wrapper method used to time functions.