zero-shot-vanilla-binary-bert

claritylab/zero-shot-vanilla-binary-bert

zero-shot-vanilla-binary-bert is a zero-shot classification model from claritylab. Use it when you need labels you did not train the model on. It is set up for zeroshot_classifier. The card lists the license as mit.

This is a BERT model. It was introduced in the Findings of ACL'23 Paper Label Agnostic Pre-training for Zero-shot Text Classification by Christopher Clarke, Yuzhao Heng, Yiping Kang, Krisztian Flautner, Lingjia Tang a…

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At a glance

Task: Zero-Shot Classification
Library: zeroshot_classifier
License: mit
Model type: bert
Access: Public
Created: May 13, 2023
Updated: May 22, 2023
SHA: 4fd8dae5

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Datasets

claritylab/UTCD

Model card

From the Hugging Face model README

Zero-shot Vanilla Binary BERT

This is a BERT model. It was introduced in the Findings of ACL'23 Paper Label Agnostic Pre-training for Zero-shot Text Classification by Christopher Clarke, Yuzhao Heng, Yiping Kang, Krisztian Flautner, Lingjia Tang and Jason Mars. The code for training and evaluating this model can be found here.

Model description

This model is intended for zero-shot text classification. It was trained under the binary classification framework as a baseline with the aspect-normalized UTCD dataset.

Finetuned from model: bert-base-uncased

Usage

Install our python package:

pip install zeroshot-classifier

Then, you can use the model like this:

>>> from zeroshot_classifier.models import BinaryBertCrossEncoder
>>> model = BinaryBertCrossEncoder(model_name='claritylab/zero-shot-vanilla-binary-bert')

>>> text = "I'd like to have this track onto my Classical Relaxations playlist."
>>> labels = [
>>>     'Add To Playlist', 'Book Restaurant', 'Get Weather', 'Play Music', 'Rate Book', 'Search Creative Work',
>>>     'Search Screening Event'
>>> ]

>>> query = [[text, lb] for lb in labels]
>>> logits = model.predict(query, apply_softmax=True)
>>> print(logits)

[[1.1909954e-04 9.9988091e-01]
 [9.9997509e-01 2.4927122e-05]
 [9.9997497e-01 2.5082643e-05]
 [2.4483365e-04 9.9975520e-01]
 [9.9996781e-01 3.2211588e-05]
 [9.9985993e-01 1.4002046e-04]
 [9.9976152e-01 2.3845369e-04]]

Task: Zero-Shot Classification
Library: zeroshot_classifier
Type: bert
License: mit
Languages: en
Created: May 13, 2023
Updated: May 22, 2023