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Bart large model for NLI-based Zero Shot Text Classification

This model uses bart-large.

Training Data

This model was trained on the MultiNLI (MNLI) dataset in the manner originally described in Yin et al. 2019.

It can be used to predict whether a topic label can be assigned to a given sequence, whether or not the label has been seen before.

Usage and Performance

The trained model can be used like this:

from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline

# Load model & tokenizer
bart_model = AutoModelForSequenceClassification.from_pretrained('navteca/bart-large-mnli')
bart_tokenizer = AutoTokenizer.from_pretrained('navteca/bart-large-mnli')

# Get predictions
nlp = pipeline('zero-shot-classification', model=bart_model, tokenizer=bart_tokenizer)

sequence = 'One day I will see the world.'
candidate_labels = ['cooking', 'dancing', 'travel']

result = nlp(sequence, candidate_labels, multi_label=True)

print(result)

#{
#  "sequence": "One day I will see the world.",
#  "labels": [
#    "travel",
#    "dancing",
#    "cooking"
#  ],
#  "scores": [
#    0.9941897988319397,
#    0.0060537424869835,
#    0.0020010927692056
#  ]
#}
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Hosted inference API
Zero-Shot Classification
Examples
Examples
This model can be loaded on the Inference API on-demand.

Dataset used to train navteca/bart-large-mnli