
# Zero-shot Explicit 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 explicit 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-explicit-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'
>>> ]

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

[[1.0987393e-03 9.9890125e-01]
 [9.9988937e-01 1.1059999e-04]
 [9.9986207e-01 1.3791372e-04]
 [1.6576477e-03 9.9834239e-01]
 [9.9990320e-01 9.6742726e-05]
 [9.9894422e-01 1.0557596e-03]
 [9.9959773e-01 4.0229000e-04]]
```
{"language": ["en"], "license": "mit", "library_name": "zeroshot_classifier", "tags": ["transformers", "sentence-transformers", "zeroshot_classifier"], "datasets": ["claritylab/UTCD"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"}
zeroshot_classifier pytorch bert text-classification transformers sentence-transformers zero-shot-classification en dataset:claritylab/UTCD license:mit region:us
TAGS
#zeroshot_classifier #pytorch #bert #text-classification #transformers #sentence-transformers #zero-shot-classification #en #dataset-claritylab/UTCD #license-mit #region-us 


# Zero-shot Explicit 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. 

## Model description

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

- Finetuned from model: 'bert-base-uncased'


## Usage

Install our python package: 


Then, you can use the model like this:


# Zero-shot Explicit 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. ## Model description

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

- Finetuned from model: 'bert-base-uncased' ## Usage

Install our python package: 


Then, you can use the model like this:
TAGS
#zeroshot_classifier #pytorch #bert #text-classification #transformers #sentence-transformers #zero-shot-classification #en #dataset-claritylab/UTCD #license-mit #region-us 
 # Zero-shot Explicit 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. ## Model description

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

- Finetuned from model: 'bert-base-uncased' ## Usage

Install our python package: 


Then, you can use the model like this: