readme: ---
  license: mit
  ---
  
  # USE THIS SPACE: https://huggingface.co/spaces/nbroad/openai-detector-base
  
  
  The following is copied from this repo: https://huggingface.co/roberta-base-openai-detector
  
  # RoBERTa Base OpenAI Detector
  
  ## Table of Contents
  - [Model Details](#model-details)
  - [Uses](#uses)
  - [Risks, Limitations and Biases](#risks-limitations-and-biases)
  - [Training](#training)
  - [Evaluation](#evaluation)
  - [Environmental Impact](#environmental-impact)
  - [Technical Specifications](#technical-specifications)
  - [Citation Information](#citation-information)
  - [Model Card Authors](#model-card-author)
  - [How To Get Started With the Model](#how-to-get-started-with-the-model)
  
  ## Model Details
  
  **Model Description:** RoBERTa base OpenAI Detector is the GPT-2 output detector model, obtained by fine-tuning a RoBERTa base model with the outputs of the 1.5B-parameter GPT-2 model. The model can be used to predict if text was generated by a GPT-2 model. This model was released by OpenAI at the same time as OpenAI released the weights of the [largest GPT-2 model](https://huggingface.co/gpt2-xl), the 1.5B parameter version. 
  
  - **Developed by:** OpenAI, see [GitHub Repo](https://github.com/openai/gpt-2-output-dataset/tree/master/detector) and [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) for full author list
  - **Model Type:** Fine-tuned transformer-based language model
  - **Language(s):** English
  - **License:** MIT
  - **Related Models:** [RoBERTa base](https://huggingface.co/roberta-base), [GPT-XL (1.5B parameter version)](https://huggingface.co/gpt2-xl), [GPT-Large (the 774M parameter version)](https://huggingface.co/gpt2-large), [GPT-Medium (the 355M parameter version)](https://huggingface.co/gpt2-medium) and [GPT-2 (the 124M parameter version)](https://huggingface.co/gpt2)
  - **Resources for more information:**
    - [Research Paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) (see, in particular, the section beginning on page 12 about Automated ML-based detection).
    - [GitHub Repo](https://github.com/openai/gpt-2-output-dataset/tree/master/detector)
    - [OpenAI Blog Post](https://openai.com/blog/gpt-2-1-5b-release/)
    - [Explore the detector model here](https://huggingface.co/openai-detector )
  
  ## Uses
  
  #### Direct Use
  
  The model is a classifier that can be used to detect text generated by GPT-2 models. 
  
  #### Downstream Use
  
  The model's developers have stated that they developed and released the model to help with research related to synthetic text generation, so the model could potentially be used for downstream tasks related to synthetic text generation. See the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) for further discussion.
  
  #### Misuse and Out-of-scope Use
  
  The model should not be used to intentionally create hostile or alienating environments for people. In addition, the model developers discuss the risk of adversaries using the model to better evade detection in their [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf), suggesting that using the model for evading detection or for supporting efforts to evade detection would be a misuse of the model. 
  
  ## Risks, Limitations and Biases
  
  **CONTENT WARNING: Readers should be aware this section may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.**
  
  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
  
  #### Risks and Limitations
  
  In their [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf), the model developers discuss the risk that the model may be used by bad actors to develop capabilities for evading detection, though one purpose of releasing the model is to help improve detection research. 
  
  In a related [blog post](https://openai.com/blog/gpt-2-1-5b-release/), the model developers also discuss the limitations of automated methods for detecting synthetic text and the need to pair automated detection tools with other, non-automated approaches. They write: 
  
  > We conducted in-house detection research and developed a detection model that has detection rates of ~95% for detecting 1.5B GPT-2-generated text. We believe this is not high enough accuracy for standalone detection and needs to be paired with metadata-based approaches, human judgment, and public education to be more effective. 
  
  The model developers also [report](https://openai.com/blog/gpt-2-1-5b-release/) finding that classifying content from larger models is more difficult, suggesting that detection with automated tools like this model will be increasingly difficult as model sizes increase. The authors find that training detector models on the outputs of larger models can improve accuracy and robustness. 
  
  #### Bias
  
  Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/202