Module base

BaseDocumentClassifier

class BaseDocumentClassifier(BaseComponent)

BaseDocumentClassifier.timing

def timing(fn, attr_name)

Wrapper method used to time functions.

Module transformers

TransformersDocumentClassifier

class TransformersDocumentClassifier(BaseDocumentClassifier)

Transformer based model for document classification using the HuggingFace's transformers framework (https://github.com/huggingface/transformers). 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 = p.run(
|        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"])
|    p.run(file_paths=file_paths)

TransformersDocumentClassifier.__init__

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: Optional[int] = None, 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 https://huggingface.co/models for full list of available models. Filter for text classification models: https://huggingface.co/models?pipeline_tag=text-classification&sort=downloads Filter for zero-shot classification models (NLI): https://huggingface.co/models?pipeline_tag=zero-shot-classification&sort=downloads&search=nli

Arguments:

• 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 https://huggingface.co/models 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: 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.

TransformersDocumentClassifier.predict

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

Returns documents containing classification result in meta field.

Documents are updated in place.

Arguments:

• documents: List of Document to classify
• batch_size: Number of Documents to classify at a time.

Returns:

List of Document enriched with meta information

TransformersDocumentClassifier.predict_batch

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.

Arguments:

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

Returns:

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