readme: ---
  pipeline_tag: sentence-similarity
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  
  ---
  
  # lambdaofgod/document-titles-nbow-nbow-mnrl
  
  This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 200 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('lambdaofgod/document-titles-nbow-nbow-mnrl')
  embeddings = model.encode(sentences)
  print(embeddings)
  ```
  
  
  
  ## 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=lambdaofgod/document-titles-nbow-nbow-mnrl)
  
  
  
  ## Full Model Architecture
  ```
  SentenceTransformer(
    (0): WordEmbeddings(
      (emb_layer): Embedding(53559, 200)
    )
    (1): WordWeights(
      (emb_layer): Embedding(53559, 1)
    )
    (2): Pooling({'word_embedding_dimension': 200, '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 -->