Zero Shot Implicit Binary Bert

🚀 Zero-shot Implicit Binary BERT

A BERT model for zero-shot text classification introduced in ACL'23 Findings

🚀 Quick Start

This is a BERT model 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. You can find the code for training and evaluating this model here.

✨ Features

• Intended for zero-shot text classification.
• Trained under the binary classification framework via implicit training with the aspect - normalized UTCD dataset.

📦 Installation

Install our python package:

pip install zeroshot-classifier

💻 Usage Examples

Basic Usage

>>> from zeroshot_classifier.models import BinaryBertCrossEncoder
>>> model = BinaryBertCrossEncoder(model_name='claritylab/zero-shot-implicit-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'
>>> ]
>>> aspect = 'intent'
>>> aspect_sep_token = model.tokenizer.additional_special_tokens[0]
>>> text = f'{aspect} {aspect_sep_token} {text}'

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

[[7.3497969e-04 9.9926502e-01]
 [9.9988127e-01 1.1870124e-04]
 [9.9988961e-01 1.1033980e-04]
 [1.9227572e-03 9.9807727e-01]
 [9.9985313e-01 1.4685343e-04]
 [9.9938977e-01 6.1021477e-04]
 [9.9838030e-01 1.6197052e-03]]

📚 Documentation

Model description

This model is intended for zero-shot text classification. It was trained under the binary classification framework via implicit training with the aspect-normalized UTCD dataset.

• Finetuned from model: bert-base-uncased

📄 License

This project is licensed under the MIT license.