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tiny-guard-4m-en-prompt-hate-speech-binary-moderation

enguard / tiny-guard-4m-en-prompt-hate-speech-binary-moderation

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Total runs: 11
24-hour runs: 0
7-day runs: 0
30-day runs: 3
Model's Last Updated: November 06 2025
text-classification

Introduction of tiny-guard-4m-en-prompt-hate-speech-binary-moderation

Model Details of tiny-guard-4m-en-prompt-hate-speech-binary-moderation

enguard/tiny-guard-4m-en-prompt-hate-speech-binary-moderation

This model is a fine-tuned Model2Vec classifier based on minishlab/potion-base-4m for the prompt-hate-speech-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/tiny-guard-4m-en-prompt-hate-speech-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.

Field Value
Classifies prompt-hate-speech-binary
Base Model minishlab/potion-base-4m
Precision 0.9198
Recall 0.7831
F1 0.8460
Confusion Matrix
True \ Predicted FAIL PASS
FAIL 196 54
PASS 17 233
Full metrics (JSON) 
{
  "FAIL": {
    "precision": 0.9198113207547169,
    "recall": 0.7831325301204819,
    "f1-score": 0.8459869848156182,
    "support": 249.0
  },
  "PASS": {
    "precision": 0.8118466898954704,
    "recall": 0.932,
    "f1-score": 0.8677839851024208,
    "support": 250.0
  },
  "accuracy": 0.8577154308617234,
  "macro avg": {
    "precision": 0.8658290053250937,
    "recall": 0.8575662650602409,
    "f1-score": 0.8568854849590195,
    "support": 499.0
  },
  "weighted avg": {
    "precision": 0.8657208243322486,
    "recall": 0.8577154308617234,
    "f1-score": 0.8569073256406696,
    "support": 499.0
  }
}
Sample Predictions
Text True Label Predicted Label
It really only works on bitchy women because they get insulted that I think they look old FAIL FAIL
You mean the Master of Mesopotamia PASS PASS
Oh fuck that's so stupid. You believed his father to be dead and you acted like a decent person. Fuck this gay earth FAIL FAIL
I'm taking it. Thanks for suggestion. PASS PASS
I think you fell for a person, not a gender. PASS PASS
It really only works on bitchy women because they get insulted that I think they look old FAIL FAIL
Prediction Speed Benchmarks
Dataset Size Time (seconds) Predictions/Second
1 0.0004 2493.64
500 0.0402 12439.95
500 0.0312 16042.59
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-4m-en-prompt-hate-speech-binary-moderation on huggingface.co

11
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7-day runs
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