zeroshot_classifier pytorch bert feature-extraction transformers sentence-transformers zero-shot-classification en dataset:claritylab/UTCD license:mit region:us
---
datasets:
- claritylab/UTCD
language:
- en
library_name: zeroshot_classifier
license: mit
metrics:
- accuracy
pipeline_tag: zero-shot-classification
tags:
- transformers
- sentence-transformers
- zeroshot_classifier
---

# Zero-shot Implicit Bi-Encoder

This is a [sentence-transformers](https://www.SBERT.net) 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](https://github.com/ChrisIsKing/zero-shot-text-classification/tree/master). 


## Model description

This model is intended for zero-shot text classification. 
It was trained under the dual encoding classification framework via implicit training with the aspect-normalized [UTCD](https://huggingface.co/datasets/claritylab/UTCD) dataset. 

- **Finetuned from model:** [`bert-base-uncased`](https://huggingface.co/bert-base-uncased)


## Usage

You can use the model like this:

```python
>>> from sentence_transformers import SentenceTransformer, util as sbert_util
>>> model = SentenceTransformer(model_name_or_path='claritylab/zero-shot-implicit-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'
>>> ]
>>> aspect = 'intent'
>>> aspect_sep_token = model.tokenizer.additional_special_tokens[0]
>>> text = f'{aspect} {aspect_sep_token} {text}'

>>> 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)

[
  0.7989747524261475,
  0.003968147560954094,
  0.027803801000118256,
  0.9257574081420898,
  0.1492517590522766,
  0.010640474036335945,
  0.012045462615787983
]
```

