readme: 
  # st1992/paraphrase-MiniLM-L12-tagalog-v2
  paraphrase-MiniLM-L12-v2 finetuned on Tagalog language: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
  ## Usage (Sentence-Transformers) : same as other sentence-transformer models
  ```
  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('st1992/paraphrase-MiniLM-L12-tagalog-v2')
  embeddings = model.encode(sentences)
  print(embeddings)
  ```
  ## Usage (HuggingFace Transformers)
  ```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 = ['hindi po', 'tulog na']
  
  # Load model from HuggingFace Hub
  tokenizer = AutoTokenizer.from_pretrained('st1992/paraphrase-MiniLM-L12-tagalog-v2')
  model = AutoModel.from_pretrained('st1992/paraphrase-MiniLM-L12-tagalog-v2')
  
  # 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)
  ```