readme: ---
  pipeline_tag: feature-extraction
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  ---
  
  # Sentence Embeddings with `bert-zwnj-wnli-mean-tokens`
  
  ## 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 = [
      'اولین حکمران شهر بابل کی بود؟',
      'در فصل زمستان چه اتفاقی افتاد؟',
      'میراث کوروش'
  ]
  model = SentenceTransformer('m3hrdadfi/bert-zwnj-wnli-mean-tokens')
  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
  
  
  # Max Pooling - Take the max value over time for every dimension. 
  def max_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()
      token_embeddings[input_mask_expanded == 0] = -1e9  # Set padding tokens to large negative value
      return torch.mean(token_embeddings, 1)[0]
  
  # Sentences we want sentence embeddings for
  sentences = [
      'اولین حکمران شهر بابل کی بود؟',
      'در فصل زمستان چه اتفاقی افتاد؟',
      'میراث کوروش'
  ]
  
  # Load model from HuggingFace Hub
  tokenizer = AutoTokenizer.from_pretrained('m3hrdadfi/bert-zwnj-wnli-mean-tokens')
  model = AutoModel.from_pretrained('m3hrdadfi/bert-zwnj-wnli-mean-tokens')
  
  # 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 = max_pooling(model_output, encoded_input['attention_mask'])
  
  print("Sentence embeddings:")
  print(sentence_embeddings)
  ```
  
  ## Questions?
  Post a Github issue from [HERE](https://github.com/m3hrdadfi/sentence-transformers).