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https://github.com/jewishlewish/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. Leveraging cutting-edge machine learning algorithms and sophisticated pattern recognition techniques.
https://github.com/jewishlewish/otis
ai anti-spam spam
Last synced: about 1 month ago
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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. Leveraging cutting-edge machine learning algorithms and sophisticated pattern recognition techniques.
- Host: GitHub
- URL: https://github.com/jewishlewish/otis
- Owner: JewishLewish
- License: bsd-3-clause
- Created: 2023-11-06T02:11:05.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2023-11-23T22:52:48.000Z (about 1 year ago)
- Last Synced: 2023-12-30T17:54:53.893Z (about 1 year ago)
- Topics: ai, anti-spam, spam
- Language: Handlebars
- Homepage: https://huggingface.co/spaces/Titeiiko/Otis
- Size: 239 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: ReadMe.MD
- License: LICENSE
Awesome Lists containing this project
README
Otis Anti-Spam AI
Go Away Spam!
» » Hugging Face
» » Github
Table of Contents
## Quickstart
```py
# pip install transformers
from transformers import pipeline
def analyze_output(input: str):
pipe = pipeline("text-classification", model="Titeiiko/OTIS-Official-Spam-Model")
x = pipe(input)[0]
if x["label"] == "LABEL_0":
return {"type":"Not Spam", "probability":x["score"]}
else:
return {"type":"Spam", "probability":x["score"]}
print(analyze_output("Cһeck out our amazinɡ bооѕting serviсe ѡhere you can get to Leveӏ 3 for 3 montһs for just 20 USD."))
#Output: {'type': 'Spam', 'probability': 0.9996588230133057}
```
## About The Project
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.
## Contributing
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**.
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".
Don't forget to give the project a star! Thanks again!
1. Fork the Project
2. Create your Feature Branch (`git checkout -b JewishLewish/Otis`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeatures'`)
4. Push to the Branch (`git push origin JewishLewish/Otis`)
5. Open a Pull Request
## License
Distributed under the BSD-3 License. See `LICENSE.txt` for more information.
## Contact
My Email: [email protected]
# OtisV1
```
{'loss': 0.2879, 'learning_rate': 4.75e-05, 'epoch': 0.5}
{'loss': 0.1868, 'learning_rate': 4.5e-05, 'epoch': 1.0}
{'eval_loss': 0.23244266211986542, 'eval_runtime': 4.2923, 'eval_samples_per_second': 465.951, 'eval_steps_per_second': 58.244, 'epoch': 1.0}
{'loss': 0.1462, 'learning_rate': 4.25e-05, 'epoch': 1.5}
{'loss': 0.1244, 'learning_rate': 4e-05, 'epoch': 2.0}
{'eval_loss': 0.19869782030582428, 'eval_runtime': 4.5759, 'eval_samples_per_second': 437.075, 'eval_steps_per_second': 54.634, 'epoch': 2.0}
{'loss': 0.0962, 'learning_rate': 3.7500000000000003e-05, 'epoch': 2.5}
{'loss': 0.07, 'learning_rate': 3.5e-05, 'epoch': 3.0}
{'eval_loss': 0.18761929869651794, 'eval_runtime': 4.1205, 'eval_samples_per_second': 485.372, 'eval_steps_per_second': 60.672, 'epoch': 3.0}
{'loss': 0.0553, 'learning_rate': 3.2500000000000004e-05, 'epoch': 3.5}
{'loss': 0.0721, 'learning_rate': 3e-05, 'epoch': 4.0}
{'eval_loss': 0.19852963089942932, 'eval_runtime': 3.992, 'eval_samples_per_second': 501.004, 'eval_steps_per_second': 62.625, 'epoch': 4.0}
{'loss': 0.0447, 'learning_rate': 2.7500000000000004e-05, 'epoch': 4.5}
{'loss': 0.0461, 'learning_rate': 2.5e-05, 'epoch': 5.0}
{'eval_loss': 0.20028768479824066, 'eval_runtime': 3.8479, 'eval_samples_per_second': 519.766, 'eval_steps_per_second': 64.971, 'epoch': 5.0}
{'loss': 0.0432, 'learning_rate': 2.25e-05, 'epoch': 5.5}
{'loss': 0.033, 'learning_rate': 2e-05, 'epoch': 6.0}
{'eval_loss': 0.20464178919792175, 'eval_runtime': 3.9167, 'eval_samples_per_second': 510.638, 'eval_steps_per_second': 63.83, 'epoch': 6.0}
{'loss': 0.0356, 'learning_rate': 1.75e-05, 'epoch': 6.5}
{'loss': 0.027, 'learning_rate': 1.5e-05, 'epoch': 7.0}
{'eval_loss': 0.20742492377758026, 'eval_runtime': 3.9716, 'eval_samples_per_second': 503.578, 'eval_steps_per_second': 62.947, 'epoch': 7.0}
{'loss': 0.0225, 'learning_rate': 1.25e-05, 'epoch': 7.5}
{'loss': 0.0329, 'learning_rate': 1e-05, 'epoch': 8.0}
{'eval_loss': 0.20604351162910461, 'eval_runtime': 4.0244, 'eval_samples_per_second': 496.964, 'eval_steps_per_second': 62.12, 'epoch': 8.0}
{'loss': 0.0221, 'learning_rate': 7.5e-06, 'epoch': 8.5}
{'loss': 0.0127, 'learning_rate': 5e-06, 'epoch': 9.0}
{'eval_loss': 0.21241146326065063, 'eval_runtime': 3.9242, 'eval_samples_per_second': 509.659, 'eval_steps_per_second': 63.707, 'epoch': 9.0}
{'loss': 0.0202, 'learning_rate': 2.5e-06, 'epoch': 9.5}
{'loss': 0.0229, 'learning_rate': 0.0, 'epoch': 10.0}
{'eval_loss': 0.2140526920557022, 'eval_runtime': 3.9546, 'eval_samples_per_second': 505.743, 'eval_steps_per_second': 63.218, 'epoch': 10.0}
{'train_runtime': 667.0781, 'train_samples_per_second': 119.926, 'train_steps_per_second': 14.991, 'train_loss': 0.07010261821746826, 'epoch': 10.0}
```