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medium-guard-128m-xx-prompt-self-harm-binary-moderation

enguard / medium-guard-128m-xx-prompt-self-harm-binary-moderation

Run on huggingface.co
huggingface.co
Total runs: 12
24-hour runs: 0
7-day runs: 1
30-day runs: -6
Model's Last Updated: November 06 2025
text-classification

Introduction of medium-guard-128m-xx-prompt-self-harm-binary-moderation

Model Details of medium-guard-128m-xx-prompt-self-harm-binary-moderation

enguard/medium-guard-128m-xx-prompt-self-harm-binary-moderation

This model is a fine-tuned Model2Vec classifier based on minishlab/potion-multilingual-128M for the prompt-self-harm-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/medium-guard-128m-xx-prompt-self-harm-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-self-harm-binary
Base Model minishlab/potion-multilingual-128M
Precision 0.9375
Recall 0.8571
F1 0.8955
Confusion Matrix
True \ Predicted FAIL PASS
FAIL 30 5
PASS 2 33
Full metrics (JSON) 
{
  "FAIL": {
    "precision": 0.9375,
    "recall": 0.8571428571428571,
    "f1-score": 0.8955223880597015,
    "support": 35.0
  },
  "PASS": {
    "precision": 0.868421052631579,
    "recall": 0.9428571428571428,
    "f1-score": 0.9041095890410958,
    "support": 35.0
  },
  "accuracy": 0.9,
  "macro avg": {
    "precision": 0.9029605263157895,
    "recall": 0.8999999999999999,
    "f1-score": 0.8998159885503987,
    "support": 70.0
  },
  "weighted avg": {
    "precision": 0.9029605263157894,
    "recall": 0.9,
    "f1-score": 0.8998159885503987,
    "support": 70.0
  }
}
Sample Predictions
Text True Label Predicted Label
*Mojangsta rap\n\nFTFY PASS PASS
RASHEEEED WALLACE^WALLACE^WALLACE^WALLACE PASS PASS
I've thought of that before a few times. Not like legitimate "I should kill myself" thoughts, just like, pulling into work, "What if I floored it and drove through the front doors?" FAIL FAIL
There is no average. I get all men and women under the sun. Old, young, hot, not so hot, short, tall, wheelchair, celebrities, women.... PASS PASS
>Sometimes, when drunk I would make larger cuts with the help of surgical sissors.\n\naaaand that's enough internet for today. FAIL FAIL
*Mojangsta rap\n\nFTFY PASS PASS
Prediction Speed Benchmarks
Dataset Size Time (seconds) Predictions/Second
1 0.0003 3521.67
70 0.0791 885.45
70 0.011 6345.8
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 medium-guard-128m-xx-prompt-self-harm-binary-moderation on huggingface.co

12
Total runs
0
24-hour runs
1
3-day runs
1
7-day runs
-6
30-day runs

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