ctrl-detector

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CTRL-detector
=====================

This directory contains the code for working with the CTRL output detector model, obtained by fine-tuning a
[RoBERTa model](https://ai.facebook.com/blog/roberta-an-optimized-method-for-pretraining-self-supervised-nlp-systems/)
with the outputs of the [CTRL model](https://github.com/salesforce/ctrl) and
 [GPT-2 model](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf). 
 Please see the
 [model card](https://github.com/salesforce/ctrl-detector/blob/master/ModelCard.pdf) for additional details.

## Downloading a pre-trained detector model

Download the weights for the fine-tuned `roberta-large` detector model (1.5 GB) as shown below. This model performs optimally for
 longer text inputs (> 32 tokens minimum), e.g., a news article.

```bash
wget https://storage.googleapis.com/sfr-ctrl-detector/combine_256.pt
```

## Running the detector model

You can launch a web UI in which you can enter a text and see the detector model's prediction
on whether or not it was generated by a CTRL/GPT-2 model or written by human.

```bash
# (on the top-level directory of this repository)
pip install -r requirements.txt
python -m server
```

After the script says "Ready to serve", nagivate to http://localhost:8080 to view the UI.

## Example script

Please see `example.py` for minimal code to run the detector model.

ctrl-detector

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