readme: ---
  language: 
    - en
  datasets:
  - SNLI
  - MNLI
  pipeline_tag: sentence-similarity
  tags:
  - zero-shot-classification
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  ---
  
  A Siamese network model trained for zero-shot and few-shot text classification.
  
  The base model is [mpnet-base](https://huggingface.co/microsoft/mpnet-base).
  It was trained on [SNLI](https://nlp.stanford.edu/projects/snli/) and [MNLI](https://cims.nyu.edu/~sbowman/multinli/).
  
  This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space.
  
  ## Usage (Sentence-Transformers)
  
  Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
  
  ```
  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('{MODEL_NAME}')
  embeddings = model.encode(sentences)
  print(embeddings)
  ```
  
  
  ## 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.
  
  ```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('{MODEL_NAME}')
  model = AutoModel.from_pretrained('{MODEL_NAME}')
  
  # 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, max pooling.
  sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
  
  print("Sentence embeddings:")
  print(sentence_embeddings)
  ```
  