vicgalle/xlm-roberta-large-xnli-anli

XLM-RoBERTa-large-XNLI-ANLI

XLM-RoBERTa-large model finetunned over several NLI datasets, ready to use for zero-shot classification.

Here are the accuracies for several test datasets:

	XNLI-es	XNLI-fr	ANLI-R1	ANLI-R2	ANLI-R3
xlm-roberta-large-xnli-anli	93.7%	93.2%	68.5%	53.6%	49.0%

The model can be loaded with the zero-shot-classification pipeline like so:

from transformers import pipeline
classifier = pipeline("zero-shot-classification", 
                       model="vicgalle/xlm-roberta-large-xnli-anli")

You can then use this pipeline to classify sequences into any of the class names you specify:

sequence_to_classify = "Algún día iré a ver el mundo"
candidate_labels = ['viaje', 'cocina', 'danza']
classifier(sequence_to_classify, candidate_labels)
#{'sequence': 'Algún día iré a ver el mundo',
#'labels': ['viaje', 'danza', 'cocina'],
#'scores': [0.9991760849952698, 0.0004178212257102132, 0.0004059972707182169]}