Update README.md

README.md

@@ -1,125 +1,65 @@
 ---
-pipeline_tag: sentence-similarity
+library_name: zeroshot_classifier
 tags:
-- sentence-transformers
-- feature-extraction
-- sentence-similarity
-- transformers
+  - transformers
+  - sentence-transformers
+  - zeroshot_classifier
+license: mit
+datasets:
+  - claritylab/UTCD
+language:
+  - en
+pipeline_tag: zero-shot-classification
+metrics:
+  - accuracy
 ---
 
-# claritylab/zero-shot-implicit-bi-encoder
+# Zero-shot Implicit Bi-Encoder
 
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+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). 
 
-<!--- Describe your model here -->
 
-## Usage (Sentence-Transformers)
+## Model description
 
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+This model was trained via the dual encoding classification framework. 
+It is intended for zero-shot text classification. 
+It was trained via implicit training with the aspect-normalized [UTCD](https://huggingface.co/datasets/claritylab/UTCD) dataset. 
 
-```
-pip install -U sentence-transformers
-```
-
-Then you can use the model like this:
-
-```python
-from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
-
-model = SentenceTransformer('claritylab/zero-shot-implicit-bi-encoder')
-embeddings = model.encode(sentences)
-print(embeddings)
-```
+- **Finetuned from model:** [`bert-base-uncased`](https://huggingface.co/bert-base-uncased)
 
 
+## Usage
 
-## Usage (HuggingFace Transformers)
-Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
+You can use the model like this:
 
 ```python
-from transformers import AutoTokenizer, AutoModel
-import torch
-
-
-#Mean Pooling - Take attention mask into account for correct averaging
-def mean_pooling(model_output, attention_mask):
-    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
-    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
-    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
-
-
-# Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
-
-# Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('claritylab/zero-shot-implicit-bi-encoder')
-model = AutoModel.from_pretrained('claritylab/zero-shot-implicit-bi-encoder')
-
-# Tokenize sentences
-encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
-
-# Compute token embeddings
-with torch.no_grad():
-    model_output = model(**encoded_input)
-
-# Perform pooling. In this case, mean pooling.
-sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
-
-print("Sentence embeddings:")
-print(sentence_embeddings)
+>>> 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'
+>>> sep_token = '<|ASPECT-SEP|>'
+>>> text = f'{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)
+
+[
+  0.6222374439239502,
+  -0.009036172181367874,
+  0.0662032887339592,
+  0.556531548500061,
+  0.09850533306598663,
+  0.3830074071884155,
+  0.11729487776756287
+]
 ```
 
-
-
-## Evaluation Results
-
-<!--- Describe how your model was evaluated -->
-
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=claritylab/zero-shot-implicit-bi-encoder)
-
-
-## Training
-The model was trained with the parameters:
-
-**DataLoader**:
-
-`torch.utils.data.dataloader.DataLoader` of length 24333 with parameters:
-```
-{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
-```
-
-**Loss**:
-
-`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 
-
-Parameters of the fit()-Method:
-```
-{
-    "epochs": 3,
-    "evaluation_steps": 100000,
-    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
-    "max_grad_norm": 1,
-    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
-    "optimizer_params": {
-        "lr": 2e-05
-    },
-    "scheduler": "WarmupLinear",
-    "steps_per_epoch": null,
-    "warmup_steps": 7300,
-    "weight_decay": 0.01
-}
-```
-
-
-## Full Model Architecture
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
-  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
-)
-```
-
-## Citing & Authors
-
-<!--- Describe where people can find more information -->