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medium-guard-128m-xx-prompt-harassment-binary-moderation

enguard / medium-guard-128m-xx-prompt-harassment-binary-moderation

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Total runs: 13
24-hour runs: 0
7-day runs: -2
30-day runs: 3
Model's Last Updated: November 06 2025
text-classification

Introduction of medium-guard-128m-xx-prompt-harassment-binary-moderation

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

enguard/medium-guard-128m-xx-prompt-harassment-binary-moderation

This model is a fine-tuned Model2Vec classifier based on minishlab/potion-multilingual-128M for the prompt-harassment-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-harassment-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-harassment-binary
Base Model minishlab/potion-multilingual-128M
Precision 0.8660
Recall 0.8034
F1 0.8336
Confusion Matrix
True \ Predicted FAIL PASS
FAIL 1593 391
PASS 247 1737
Full metrics (JSON) 
{
  "FAIL": {
    "precision": 0.8660130718954249,
    "recall": 0.8034360788276907,
    "f1-score": 0.8335517693315858,
    "support": 1979.0
  },
  "PASS": {
    "precision": 0.8157271435338702,
    "recall": 0.875,
    "f1-score": 0.8443245893601373,
    "support": 1968.0
  },
  "accuracy": 0.839118317709653,
  "macro avg": {
    "precision": 0.8408701077146475,
    "recall": 0.8392180394138453,
    "f1-score": 0.8389381793458616,
    "support": 3947.0
  },
  "weighted avg": {
    "precision": 0.8409401793148473,
    "recall": 0.839118317709653,
    "f1-score": 0.8389231678155457,
    "support": 3947.0
  }
}
Sample Predictions
Text True Label Predicted Label
you don't have much experience with european gypsies, do you?

Touch one, and they spawn cousins all around you like an RPG boss. 		FAIL FAIL
良い、悪天候時butthole! FAIL FAIL
I know what you are thinking....but where will he build the fire for the smoke signals? PASS PASS
Not really even an "unnatural" disaster. Oil comes from the earth, water comes from the earth. Its inevitable that they will find each other. PASS PASS
You don't even know what you were quoting, do you? You just saw it on the internet somewhere and decided to parrot it here on reddit for karma.

Nicely done, I'll give you a C for effort, but you should try harder next time.

FAIL PASS
you don't have much experience with european gypsies, do you?

Touch one, and they spawn cousins all around you like an RPG boss. 		FAIL FAIL
Prediction Speed Benchmarks
Dataset Size Time (seconds) Predictions/Second
1 0.0003 3688.92
1000 0.1995 5012.0
3968 0.4654 8525.49
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-harassment-binary-moderation on huggingface.co

13
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0
24-hour runs
0
3-day runs
-2
7-day runs
3
30-day runs

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