readme: ---
  pipeline_tag: sentence-similarity
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  language: 
    - it
  ---
  
  # sentence-BERTino-3-64
  
  This is a distilled [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 64 dimensional dense vector space and can be used for tasks like clustering or semantic search.
  
  <!--- Describe your model here -->
  
  ## 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('efederici/sentence-BERTino-3-64')
  embeddings = model.encode(sentences)
  print(embeddings)
  ```
  
  
  ## Training
  The model was trained with the parameters:
  
  **DataLoader**:
  
  `torch.utils.data.dataloader.DataLoader` of length 1724 with parameters:
  ```
  {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
  ```
  
  **Loss**:
  
  `sentence_transformers.losses.MSELoss.MSELoss` 
  
  
  ## Full Model Architecture
  ```
  SentenceTransformer(
    (0): SentenceTransformer(
      (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel 
      (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})
    )
    (1): Dense({'in_features': 768, 'out_features': 64, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
  )
  ```
  
  ## Citing & Authors
  
  <!--- Describe where people can find more information -->