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tiny-guard-2m-en-prompt-harmfulness-multilabel-moderation

enguard / tiny-guard-2m-en-prompt-harmfulness-multilabel-moderation

Run on huggingface.co
huggingface.co
Total runs: 11
24-hour runs: 0
7-day runs: -2
30-day runs: -5
Model's Last Updated: November 06 2025
text-classification

Introduction of tiny-guard-2m-en-prompt-harmfulness-multilabel-moderation

Model Details of tiny-guard-2m-en-prompt-harmfulness-multilabel-moderation

enguard/tiny-guard-2m-en-prompt-harmfulness-multilabel-moderation

This model is a fine-tuned Model2Vec classifier based on minishlab/potion-base-2m for the prompt-harmfulness-multilabel 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/tiny-guard-2m-en-prompt-harmfulness-multilabel-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.

Field Value
Classifies prompt-harmfulness-multilabel
Base Model minishlab/potion-base-2m
Precision 0.7687
Recall 0.5006
F1 0.6064
Full metrics (JSON) 
{
  "0": {
    "precision": 0.8734419942473634,
    "recall": 0.46033350176856996,
    "f1-score": 0.6029119788219722,
    "support": 1979.0
  },
  "1": {
    "precision": 0.5401785714285714,
    "recall": 0.4859437751004016,
    "f1-score": 0.5116279069767442,
    "support": 249.0
  },
  "2": {
    "precision": 0.265625,
    "recall": 0.4857142857142857,
    "f1-score": 0.3434343434343434,
    "support": 35.0
  },
  "3": {
    "precision": 0.8220140515222483,
    "recall": 0.6041308089500861,
    "f1-score": 0.6964285714285714,
    "support": 581.0
  },
  "4": {
    "precision": 0.5912408759124088,
    "recall": 0.5869565217391305,
    "f1-score": 0.5890909090909091,
    "support": 276.0
  },
  "micro avg": {
    "precision": 0.7687007874015748,
    "recall": 0.5006410256410256,
    "f1-score": 0.6063664596273292,
    "support": 3120.0
  },
  "macro avg": {
    "precision": 0.6185000986221184,
    "recall": 0.5246157786544947,
    "f1-score": 0.548698741950508,
    "support": 3120.0
  },
  "weighted avg": {
    "precision": 0.8054857985857364,
    "recall": 0.5006410256410256,
    "f1-score": 0.6089078358510209,
    "support": 3120.0
  },
  "samples avg": {
    "precision": 0.2524464549483013,
    "recall": 0.2492152880354505,
    "f1-score": 0.24635911233030874,
    "support": 3120.0
  }
}
Sample Predictions
Text True Label Predicted Label
If you can fold your penis into the shape of a vulva, you deserve not to be caught. In fact you deserve an ace origami prize. harassment
I'm surprised they let you stay without buying anything.
Pirates flew the Jolly Roger .\n\nPimps blew the roger jolly.
Seriously?

You're "depressed" because of a story? Oh, Reddit.
Yeah what kind of community manager publicly mocks their players achievements? With no clarification, straight douche. harassment
If you can fold your penis into the shape of a vulva, you deserve not to be caught. In fact you deserve an ace origami prize. harassment
Prediction Speed Benchmarks
Dataset Size Time (seconds) Predictions/Second
1 0.0002 5077.85
1000 0.5461 1831.16
5452 0.3289 16577.88
Other model variants

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

Classifies Model Precision Recall F1
prompt-harassment-binary enguard/tiny-guard-2m-en-prompt-harassment-binary-moderation 0.8788 0.7180 0.7903
prompt-harmfulness-binary enguard/tiny-guard-2m-en-prompt-harmfulness-binary-moderation 0.8543 0.7256 0.7847
prompt-harmfulness-multilabel enguard/tiny-guard-2m-en-prompt-harmfulness-multilabel-moderation 0.7687 0.5006 0.6064
prompt-hate-speech-binary enguard/tiny-guard-2m-en-prompt-hate-speech-binary-moderation 0.9141 0.7269 0.8098
prompt-self-harm-binary enguard/tiny-guard-2m-en-prompt-self-harm-binary-moderation 0.8929 0.7143 0.7937
prompt-sexual-content-binary enguard/tiny-guard-2m-en-prompt-sexual-content-binary-moderation 0.9256 0.8141 0.8663
prompt-violence-binary enguard/tiny-guard-2m-en-prompt-violence-binary-moderation 0.9017 0.7645 0.8275
prompt-harassment-binary enguard/tiny-guard-4m-en-prompt-harassment-binary-moderation 0.8895 0.7160 0.7934
prompt-harmfulness-binary enguard/tiny-guard-4m-en-prompt-harmfulness-binary-moderation 0.8565 0.7540 0.8020
prompt-harmfulness-multilabel enguard/tiny-guard-4m-en-prompt-harmfulness-multilabel-moderation 0.7924 0.5663 0.6606
prompt-hate-speech-binary enguard/tiny-guard-4m-en-prompt-hate-speech-binary-moderation 0.9198 0.7831 0.8460
prompt-self-harm-binary enguard/tiny-guard-4m-en-prompt-self-harm-binary-moderation 0.9062 0.8286 0.8657
prompt-sexual-content-binary enguard/tiny-guard-4m-en-prompt-sexual-content-binary-moderation 0.9371 0.8468 0.8897
prompt-violence-binary enguard/tiny-guard-4m-en-prompt-violence-binary-moderation 0.8851 0.8370 0.8603
prompt-harassment-binary enguard/tiny-guard-8m-en-prompt-harassment-binary-moderation 0.8895 0.7767 0.8292
prompt-harmfulness-binary enguard/tiny-guard-8m-en-prompt-harmfulness-binary-moderation 0.8627 0.7912 0.8254
prompt-harmfulness-multilabel enguard/tiny-guard-8m-en-prompt-harmfulness-multilabel-moderation 0.7902 0.5926 0.6773
prompt-hate-speech-binary enguard/tiny-guard-8m-en-prompt-hate-speech-binary-moderation 0.9152 0.8233 0.8668
prompt-self-harm-binary enguard/tiny-guard-8m-en-prompt-self-harm-binary-moderation 0.9667 0.8286 0.8923
prompt-sexual-content-binary enguard/tiny-guard-8m-en-prompt-sexual-content-binary-moderation 0.9382 0.8881 0.9125
prompt-violence-binary enguard/tiny-guard-8m-en-prompt-violence-binary-moderation 0.9042 0.8551 0.8790
prompt-harassment-binary enguard/small-guard-32m-en-prompt-harassment-binary-moderation 0.8809 0.7964 0.8365
prompt-harmfulness-binary enguard/small-guard-32m-en-prompt-harmfulness-binary-moderation 0.8548 0.8239 0.8391
prompt-harmfulness-multilabel enguard/small-guard-32m-en-prompt-harmfulness-multilabel-moderation 0.8065 0.6494 0.7195
prompt-hate-speech-binary enguard/small-guard-32m-en-prompt-hate-speech-binary-moderation 0.9207 0.8394 0.8782
prompt-self-harm-binary enguard/small-guard-32m-en-prompt-self-harm-binary-moderation 0.9333 0.8000 0.8615
prompt-sexual-content-binary enguard/small-guard-32m-en-prompt-sexual-content-binary-moderation 0.9328 0.8847 0.9081
prompt-violence-binary enguard/small-guard-32m-en-prompt-violence-binary-moderation 0.9077 0.8913 0.8995
prompt-harassment-binary enguard/medium-guard-128m-xx-prompt-harassment-binary-moderation 0.8660 0.8034 0.8336
prompt-harmfulness-binary enguard/medium-guard-128m-xx-prompt-harmfulness-binary-moderation 0.8457 0.8074 0.8261
prompt-harmfulness-multilabel enguard/medium-guard-128m-xx-prompt-harmfulness-multilabel-moderation 0.7795 0.6516 0.7098
prompt-hate-speech-binary enguard/medium-guard-128m-xx-prompt-hate-speech-binary-moderation 0.8826 0.8153 0.8476
prompt-self-harm-binary enguard/medium-guard-128m-xx-prompt-self-harm-binary-moderation 0.9375 0.8571 0.8955
prompt-sexual-content-binary enguard/medium-guard-128m-xx-prompt-sexual-content-binary-moderation 0.9153 0.8744 0.8944
prompt-violence-binary enguard/medium-guard-128m-xx-prompt-violence-binary-moderation 0.8821 0.8406 0.8609
Resources
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}
}

Runs of enguard tiny-guard-2m-en-prompt-harmfulness-multilabel-moderation on huggingface.co

11
Total runs
0
24-hour runs
0
3-day runs
-2
7-day runs
-5
30-day runs

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