Update README.md

README.md

@@ -1,45 +1,39 @@
 ---
-model-index:
-- name: zero-shot-vanilla-binary-bert
-  results: []
+tags:
+- transformers
+- sentence-transformers
+- zeroshot_classifier
 ---
 
-<!-- This model card has been generated automatically according to the information Keras had access to. You should
-probably proofread and complete it, then remove this comment. -->
-
-# zero-shot-vanilla-binary-bert
-
-This model was trained from scratch on an unknown dataset.
-It achieves the following results on the evaluation set:
+# Zero-shot Vanilla Binary BERT 
 
+This model 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
 
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
+This model was trained via the binary classification framework. It is intended for zero-shot text classification. 
+It was trained and evaluated as a baseline with the aspect-normalized [UTCD](https://huggingface.co/datasets/claritylab/UTCD) dataset. 
 
-### Training hyperparameters
+- **Finetuned from model:** [`bert-base-uncased`](https://huggingface.co/bert-base-uncased)
 
-The following hyperparameters were used during training:
-- optimizer: None
-- training_precision: float32
 
-### Training results
+## Usage
 
+You can use the model like this:
 
+```python
+from zeroshot_classifier.models import BinaryBertCrossEncoder
+model = BinaryBertCrossEncoder(model_name='claritylab/zero-shot-vanilla-binary-bert')
 
-### Framework versions
+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)
+```
 
-- Transformers 4.16.2
-- TensorFlow 2.12.0
-- Datasets 2.12.0
-- Tokenizers 0.11.0