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--- |
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license: bsd-3-clause |
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language: |
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- en |
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tags: |
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- anti-spam |
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- spam |
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--- |
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<!-- PROJECT LOGO --> |
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<br /> |
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<div align="center"> |
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<a href="https://github.com/JewishLewish/Otis"> |
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<img src="https://cdn.discordapp.com/attachments/660227834500874276/1175310288212463706/47._Big_Tree_1.png?ex=656ac400&is=65584f00&hm=0518b63834cd0da8208e79c1b019fd41e170aaa860d4812695fb8a43d43abc55&" alt="Logo" width="200" height="200"> |
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</a> |
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<h3 align="center">Otis Anti-Spam AI</h3> |
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<p align="center"> |
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Go Away Spam! |
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<br /> |
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<a href="https://huggingface.co/Titeiiko/OTIS-Official-Spam-Model"><strong>» » Hugging Face</strong></a> |
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<br /> |
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<a href="https://github.com/JewishLewish/Otis"><strong>» » Github</strong></a> |
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<div align="center"> |
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</div> |
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</p> |
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</div> |
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<!-- TABLE OF CONTENTS --> |
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<details> |
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<summary>Table of Contents</summary> |
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<ol> |
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<li> |
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<a href="#Quickstart">Quickstart</a> |
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</li> |
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<li><a href="#contributing">Contributing</a></li> |
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<li><a href="#license">License</a></li> |
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<li><a href="#contact">Contact</a></li> |
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</ol> |
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</details> |
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<!-- Quickstar --> |
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## Quickstart |
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```py |
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# pip install transformers |
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from transformers import pipeline |
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def analyze_output(input: str): |
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pipe = pipeline("text-classification", model="Titeiiko/OTIS-Official-Spam-Model") |
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x = pipe(input)[0] |
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if x["label"] == "LABEL_0": |
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return {"type":"Not Spam", "probability":x["score"]} |
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else: |
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return {"type":"Spam", "probability":x["score"]} |
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print(analyze_output("Cһeck out our amazinɡ bооѕting serviсe ѡhere you can get to Leveӏ 3 for 3 montһs for just 20 USD.")) |
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#Output: {'type': 'Spam', 'probability': 0.9996588230133057} |
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``` |
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<!-- ABOUT THE PROJECT --> |
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## About The Project |
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Introducing Otis: Otis is an advanced anti-spam artificial intelligence model designed to mitigate and combat the proliferation of unwanted and malicious content within digital communication channels. |
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<p align="right">(<a href="#readme-top">back to top</a>)</p> |
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<!-- CONTRIBUTING --> |
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## Contributing |
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Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**. |
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If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". |
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Don't forget to give the project a star! Thanks again! |
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1. Fork the Project |
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2. Create your Feature Branch (`git checkout -b JewishLewish/Otis`) |
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3. Commit your Changes (`git commit -m 'Add some AmazingFeatures'`) |
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4. Push to the Branch (`git push origin JewishLewish/Otis`) |
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5. Open a Pull Request |
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<p align="right">(<a href="#readme-top">back to top</a>)</p> |
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<!-- LICENSE --> |
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## License |
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Distributed under the BSD-3 License. See `LICENSE.txt` for more information. |
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<p align="right">(<a href="#readme-top">back to top</a>)</p> |
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<!-- CONTACT --> |
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## Contact |
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My Email: lenny@lunes.host |
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<p align="right">(<a href="#readme-top">back to top</a>)</p> |
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# OtisV1 |
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``` |
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{'loss': 0.2879, 'learning_rate': 4.75e-05, 'epoch': 0.5} |
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{'loss': 0.1868, 'learning_rate': 4.5e-05, 'epoch': 1.0} |
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{'eval_loss': 0.23244266211986542, 'eval_runtime': 4.2923, 'eval_samples_per_second': 465.951, 'eval_steps_per_second': 58.244, 'epoch': 1.0} |
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{'loss': 0.1462, 'learning_rate': 4.25e-05, 'epoch': 1.5} |
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{'loss': 0.1244, 'learning_rate': 4e-05, 'epoch': 2.0} |
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{'eval_loss': 0.19869782030582428, 'eval_runtime': 4.5759, 'eval_samples_per_second': 437.075, 'eval_steps_per_second': 54.634, 'epoch': 2.0} |
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{'loss': 0.0962, 'learning_rate': 3.7500000000000003e-05, 'epoch': 2.5} |
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{'loss': 0.07, 'learning_rate': 3.5e-05, 'epoch': 3.0} |
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{'eval_loss': 0.18761929869651794, 'eval_runtime': 4.1205, 'eval_samples_per_second': 485.372, 'eval_steps_per_second': 60.672, 'epoch': 3.0} |
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{'loss': 0.0553, 'learning_rate': 3.2500000000000004e-05, 'epoch': 3.5} |
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{'loss': 0.0721, 'learning_rate': 3e-05, 'epoch': 4.0} |
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{'eval_loss': 0.19852963089942932, 'eval_runtime': 3.992, 'eval_samples_per_second': 501.004, 'eval_steps_per_second': 62.625, 'epoch': 4.0} |
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{'loss': 0.0447, 'learning_rate': 2.7500000000000004e-05, 'epoch': 4.5} |
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{'loss': 0.0461, 'learning_rate': 2.5e-05, 'epoch': 5.0} |
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{'eval_loss': 0.20028768479824066, 'eval_runtime': 3.8479, 'eval_samples_per_second': 519.766, 'eval_steps_per_second': 64.971, 'epoch': 5.0} |
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{'loss': 0.0432, 'learning_rate': 2.25e-05, 'epoch': 5.5} |
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{'loss': 0.033, 'learning_rate': 2e-05, 'epoch': 6.0} |
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{'eval_loss': 0.20464178919792175, 'eval_runtime': 3.9167, 'eval_samples_per_second': 510.638, 'eval_steps_per_second': 63.83, 'epoch': 6.0} |
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{'loss': 0.0356, 'learning_rate': 1.75e-05, 'epoch': 6.5} |
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{'loss': 0.027, 'learning_rate': 1.5e-05, 'epoch': 7.0} |
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{'eval_loss': 0.20742492377758026, 'eval_runtime': 3.9716, 'eval_samples_per_second': 503.578, 'eval_steps_per_second': 62.947, 'epoch': 7.0} |
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{'loss': 0.0225, 'learning_rate': 1.25e-05, 'epoch': 7.5} |
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{'loss': 0.0329, 'learning_rate': 1e-05, 'epoch': 8.0} |
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{'eval_loss': 0.20604351162910461, 'eval_runtime': 4.0244, 'eval_samples_per_second': 496.964, 'eval_steps_per_second': 62.12, 'epoch': 8.0} |
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{'loss': 0.0221, 'learning_rate': 7.5e-06, 'epoch': 8.5} |
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{'loss': 0.0127, 'learning_rate': 5e-06, 'epoch': 9.0} |
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{'eval_loss': 0.21241146326065063, 'eval_runtime': 3.9242, 'eval_samples_per_second': 509.659, 'eval_steps_per_second': 63.707, 'epoch': 9.0} |
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{'loss': 0.0202, 'learning_rate': 2.5e-06, 'epoch': 9.5} |
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{'loss': 0.0229, 'learning_rate': 0.0, 'epoch': 10.0} |
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{'eval_loss': 0.2140526920557022, 'eval_runtime': 3.9546, 'eval_samples_per_second': 505.743, 'eval_steps_per_second': 63.218, 'epoch': 10.0} |
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{'train_runtime': 667.0781, 'train_samples_per_second': 119.926, 'train_steps_per_second': 14.991, 'train_loss': 0.07010261821746826, 'epoch': 10.0} |
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``` |
