
# Zero-shot Vanilla 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 as a baseline 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-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
]
```


{"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 feature-extraction transformers sentence-transformers zero-shot-classification en dataset:claritylab/UTCD license:mit region:us
TAGS
#zeroshot_classifier #pytorch #bert #feature-extraction #transformers #sentence-transformers #zero-shot-classification #en #dataset-claritylab/UTCD #license-mit #region-us 


# 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:




# 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:
TAGS
#zeroshot_classifier #pytorch #bert #feature-extraction #transformers #sentence-transformers #zero-shot-classification #en #dataset-claritylab/UTCD #license-mit #region-us 
 # 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: