readme: ---
  pipeline_tag: text-classification
  tags:
  - sentence-transformers
  - sentence-similarity
  - transformers
  language:
  - en
  - da
  licence:
  - apache-2.0
  ---
  # SetFit-caesar-cipher-classifier
  This was a [sentence-transformers](https://www.SBERT.net) model: It mapped sentences & paragraphs to a 768 dimensional dense vector space and could be used for tasks like clustering or semantic search. Now it's a SetFit classifier, determining if a sentence is gibberish or not. Hail Science!
  <!--- Describe your model here -->
  ## Usage (SetFitModel)
  Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) and [SetFit](https://github.com/huggingface/setfit) installed:
  ```
  pip install -U sentence-transformers setfit
  ```
  Then you can use the model like this:
  ```python
  from setfit import SetFitModel
  sentences = ["This is an example sentence", "Each sentence is tested", "Aopz pz hu lehtwsl zlualujl", "Rnpu fragrapr vf grfgrq"]
  model = SetFitModel.from_pretrained("trollek/setfit-gibberish-detector")
  for sentence in sentences:
    classification = model.predict(sentence)
    print(classification)
  
  ```
  
   - 0 is clear text
   - 1 is gibberish
  
  It would presumably work on Enigma encrypted text, but tests would have to be done. Anyway, the model has proven pretty reliable (99%) in classifying english and danish sentences.
  