small-guard-32m-en-prompt-sexual-content-binary-moderation

enguard/small-guard-32m-en-prompt-sexual-content-binary-moderation

This model is a fine-tuned Model2Vec classifier based on minishlab/potion-base-32m for the prompt-sexual-content-binary found in the enguard/multi-lingual-prompt-moderation dataset.

Installation

pip install model2vec[inference]

Usage

from model2vec.inference import StaticModelPipeline

model = StaticModelPipeline.from_pretrained(
  "enguard/small-guard-32m-en-prompt-sexual-content-binary-moderation"
)


# Supports single texts. Format input as a single text:
text = "Example sentence"

model.predict([text])
model.predict_proba([text])

Why should you use these models?

• Optimized for precision to reduce false positives.
• Extremely fast inference: up to x500 faster than SetFit.

This model variant

Below is a quick overview of the model variant and core metrics.

Confusion Matrix

<details> <summary><b>Full metrics (JSON)</b></summary>

{
  "FAIL": {
    "precision": 0.9328493647912885,
    "recall": 0.8846815834767642,
    "f1-score": 0.9081272084805654,
    "support": 581.0
  },
  "PASS": {
    "precision": 0.8910569105691057,
    "recall": 0.9367521367521368,
    "f1-score": 0.9133333333333333,
    "support": 585.0
  },
  "accuracy": 0.9108061749571184,
  "macro avg": {
    "precision": 0.9119531376801971,
    "recall": 0.9107168601144504,
    "f1-score": 0.9107302709069494,
    "support": 1166.0
  },
  "weighted avg": {
    "precision": 0.9118814525100046,
    "recall": 0.9108061749571184,
    "f1-score": 0.9107392007952045,
    "support": 1166.0
  }
}

</details> <details> <summary><b>Sample Predictions</b></summary> </details> <details> <summary><b>Prediction Speed Benchmarks</b></summary> </details>

Other model variants

Below is a general overview of the best-performing models for each dataset variant.

Resources

• Awesome AI Guardrails: https://github.com/enguard-ai/awesome-ai-guardails
• Model2Vec: https://github.com/MinishLab/model2vec
• Docs: https://minish.ai/packages/model2vec/introduction

Citation

If you use this model, please cite Model2Vec:

@software{minishlab2024model2vec,
  author       = {Stephan Tulkens and {van Dongen}, Thomas},
  title        = {Model2Vec: Fast State-of-the-Art Static Embeddings},
  year         = {2024},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.17270888},
  url          = {https://github.com/MinishLab/model2vec},
  license      = {MIT}
}