Zero-shot Vanilla Bi-Encoder
This is a sentence-transformers 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 dual encoding classification framework as a baseline with the aspect-normalized UTCD dataset.
- Finetuned from model:
bert-base-uncased
Usage
You can use the model like this:
>>> 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
]
- Downloads last month
- 8
Inference API (serverless) does not yet support zeroshot_classifier models for this pipeline type.