Module base

BaseDocumentClassifier Objects

class BaseDocumentClassifier(BaseComponent)

timing

| timing(fn, attr_name)

Wrapper method used to time functions.

Module transformers

TransformersDocumentClassifier Objects

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, ...} }

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

Usage example: ... retriever = ElasticsearchRetriever(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"]

__init__

| __init__(model_name_or_path: str = "bhadresh-savani/distilbert-base-uncased-emotion", model_version: Optional[str] = None, tokenizer: Optional[str] = None, use_gpu: int = 0, return_all_scores: bool = False, task: str = 'text-classification', labels: Optional[List[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: If < 0, then use cpu. If >= 0, this is the ordinal of the gpu to use
• 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.

predict

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

Returns documents containing classification result in meta field

Arguments:

• documents: List of Document to classify

Returns:

List of Document enriched with meta information