File size: 10,564 Bytes
0d5ae6b 4305192 d76bb1e 4305192 5254e18 4305192 01e7bcc 4305192 d76bb1e 4305192 01e7bcc 4305192 5a67442 4305192 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 |
---
license: bsd-3-clause
language:
- en
tags:
- anti-spam
- spam
---
<!-- PROJECT LOGO -->
<br />
<div align="center">
<a href="https://github.com/JewishLewish/Otis">
<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">
</a>
<h3 align="center">Otis Anti-Spam AI</h3>
<p align="center">
Go Away Spam!
<br />
<a href="https://huggingface.co/Titeiiko/OTIS-Official-Spam-Model"><strong>» » Hugging Face</strong></a>
<br />
<a href="https://github.com/JewishLewish/Otis"><strong>» » Github</strong></a>
<br />
<div align="center">
![GitHub forks](https://img.shields.io/github/forks/JewishLewish/otis?color=63C9A4&style=for-the-badge)
![GitHub Repo stars](https://img.shields.io/github/stars/JewishLewish/otis?color=63C9A4&style=for-the-badge)
![GitHub](https://img.shields.io/github/license/JewishLewish/otis?color=63C9A4&style=for-the-badge)
![GitHub code size in bytes](https://img.shields.io/github/languages/code-size/JewishLewish/otis?color=63C9A4&style=for-the-badge)
</div>
</p>
</div>
<!-- TABLE OF CONTENTS -->
<details>
<summary>Table of Contents</summary>
<ol>
<li>
<a href="#Quickstart">Quickstart</a>
</li>
<li><a href="#contributing">Contributing</a></li>
<li><a href="#license">License</a></li>
<li><a href="#contact">Contact</a></li>
</ol>
</details>
<!-- Quickstar -->
## 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 -->
## 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.
<p align="right">(<a href="#readme-top">back to top</a>)</p>
<!-- CONTRIBUTING -->
## 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
<p align="right">(<a href="#readme-top">back to top</a>)</p>
<!-- LICENSE -->
## License
Distributed under the BSD-3 License. See `LICENSE.txt` for more information.
<p align="right">(<a href="#readme-top">back to top</a>)</p>
<!-- CONTACT -->
## Contact
My Email: lenny@lunes.host
<p align="right">(<a href="#readme-top">back to top</a>)</p>
# 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}
``` |