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]}
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Zero-Shot Classification
This model can be loaded on the Inference API on-demand.