README.md

---
pipeline_tag: text-classification
tags:
- sentence-transformers
- 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!

## 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.