README.md

---
library_name: zeroshot_classifier
tags:
  - transformers
  - sentence-transformers
  - zeroshot_classifier
license: mit
datasets:
  - claritylab/UTCD
language:
  - en
pipeline_tag: zero-shot-classification
metrics:
  - accuracy
---

# Zero-shot Implicit 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](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 binary 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

Install our [python package](https://pypi.org/project/zeroshot-classifier/): 
```bash
pip install zeroshot-classifier
```

Then, you can use the model like this:

```python
>>> 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]]
```