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detector/README.md
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GPT-2 Output Detector
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=====================
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This directory contains the code for working with the GPT-2 output detector model, obtained by fine-tuning a
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[RoBERTa model](https://ai.facebook.com/blog/roberta-an-optimized-method-for-pretraining-self-supervised-nlp-systems/)
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with [the outputs of the 1.5B-parameter GPT-2 model](https://github.com/openai/gpt-2-output-dataset).
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For motivations and discussions regarding the release of this detector model, please check out
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[our blog post](https://openai.com/blog/gpt-2-1-5b-release/) and [report](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf).
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## Downloading a pre-trained detector model
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Download the weights for the fine-tuned `roberta-base` model (478 MB):
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```bash
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wget https://storage.googleapis.com/gpt-2/detector-models/v1/detector-base.pt
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```
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or `roberta-large` model (1.5 GB):
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```bash
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wget https://storage.googleapis.com/gpt-2/detector-models/v1/detector-large.pt
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```
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These RoBERTa-based models are fine-tuned with a mixture of temperature-1 and nucleus sampling outputs,
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which should generalize well to outputs generated using different sampling methods.
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## Running a detector model
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You can launch a web UI in which you can enter a text and see the detector model's prediction
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on whether or not it was generated by a GPT-2 model.
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```bash
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# (on the top-level directory of this repository)
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pip install -r requirements.txt
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python -m detector.server detector-base.pt
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```
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After the script says "Ready to serve", nagivate to http://localhost:8080 to view the UI.
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## Training a new detector model
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You can use the provided training script to train a detector model on a new set of datasets.
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We recommend using a GPU machine for this task.
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```bash
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# (on the top-level directory of this repository)
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pip install -r requirements.txt
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python -m detector.train
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```
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The training script supports a number of different options; append `--help` to the command above for usage.
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