Zero Shot Vanilla Bi Encoder

🚀 Zero-shot Vanilla Bi-Encoder

A sentence-transformers model for zero-shot text classification

This is a sentence-transformers 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. The code for training and evaluating this model can be found here.

✨ Features

• Designed for zero-shot text classification.
• Trained as a baseline under the dual encoding classification framework with the aspect-normalized UTCD dataset.
• Finetuned from the bert-base-uncased model.

📦 Installation

No specific installation steps are provided in the original README.

💻 Usage Examples

Basic Usage

>>> from sentence_transformers import SentenceTransformer, util as sbert_util
>>> model = SentenceTransformer(model_name_or_path='claritylab/zero-shot-vanilla-bi-encoder')

>>> 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'
>>> ]

>>> text_embed = model.encode(text)
>>> label_embeds = model.encode(labels)
>>> scores = [sbert_util.cos_sim(text_embed, lb_embed).item() for lb_embed in label_embeds]
>>> print(scores)

[
  0.7219685912132263,
  -0.011121425777673721,
  0.04929959028959274,
  0.6653788089752197,
  0.07093366980552673,
  0.2897151708602905,
  0.06133288890123367
]

📚 Documentation

Model description

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

• Finetuned from model: bert-base-uncased

📄 License

This project is licensed under the MIT license.