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medium-guard-128m-xx-general-politeness-binary-intel

enguard / medium-guard-128m-xx-general-politeness-binary-intel

Run on huggingface.co
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Total runs: 12
24-hour runs: 0
7-day runs: 4
30-day runs: 5
Model's Last Updated: November 06 2025
text-classification

Introduction of medium-guard-128m-xx-general-politeness-binary-intel

Model Details of medium-guard-128m-xx-general-politeness-binary-intel

enguard/medium-guard-128m-xx-general-politeness-binary-intel

This model is a fine-tuned Model2Vec classifier based on minishlab/potion-multilingual-128M for the general-politeness-binary found in the Intel/polite-guard dataset.

Installation 
pip install model2vec[inference]
Usage 
from model2vec.inference import StaticModelPipeline

model = StaticModelPipeline.from_pretrained(
  "enguard/medium-guard-128m-xx-general-politeness-binary-intel"
)

model.predict(["Example sentence"])
model.predict_proba(["Example sentence"])
Why should you use these models?
  • Optimized for precision to reduce false positives.
  • Extremely fast inference using static embeddings powered by Model2Vec.
This model variant

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

Field Value
Classifies general-politeness-binary
Base Model minishlab/potion-multilingual-128M
F1 0.9925
Full metrics (JSON) 
{
  "FAIL": {
    "precision": 0.9755908562572646,
    "recall": 0.9944707740916272,
    "f1-score": 0.984940348132212,
    "support": 2532.0
  },
  "PASS": {
    "precision": 0.9981624885155532,
    "recall": 0.9917840375586855,
    "f1-score": 0.9949630404919212,
    "support": 7668.0
  },
  "accuracy": 0.9924509803921568,
  "macro avg": {
    "precision": 0.9868766723864089,
    "recall": 0.9931274058251564,
    "f1-score": 0.9899516943120665,
    "support": 10200.0
  },
  "weighted avg": {
    "precision": 0.9925594127432016,
    "recall": 0.9924509803921568,
    "f1-score": 0.992475054506158,
    "support": 10200.0
  }
}
Sample Predictions
Text True Label Predicted Label
I appreciate your interest in our vegetarian options. I can provide you with a list of our current dishes that cater to your dietary preferences. PASS PASS
I understand you're concerned about the ski lessons, and I'll look into the options for rescheduling. PASS PASS
Our technical skills course will cover the essential topics in data analysis, including data visualization and statistical modeling. The course materials will be available on our learning platform. PASS PASS
Our buffet hours are from 11 AM to 9 PM. Please note that we have a limited selection of options available during the lunch break. PASS PASS
I'll look into your policy details and see what options are available to you. PASS PASS
I appreciate your interest in our vegetarian options. I can provide you with a list of our current dishes that cater to your dietary preferences. PASS PASS
Prediction Speed Benchmarks
Dataset Size Time (seconds) Predictions/Second
1 0.0005 2132.34
1000 0.039 25665.32
10000 0.5205 19212.92
Other model variants

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

Classifies Model F1
general-politeness-binary enguard/small-guard-32m-en-general-politeness-binary-intel 0.9939
general-politeness-binary enguard/tiny-guard-8m-en-general-politeness-binary-intel 0.9928
general-politeness-binary enguard/tiny-guard-2m-en-general-politeness-binary-intel 0.9927
general-politeness-binary enguard/medium-guard-128m-xx-general-politeness-binary-intel 0.9925
general-politeness-binary enguard/tiny-guard-4m-en-general-politeness-binary-intel 0.9925
general-politeness-multilabel enguard/small-guard-32m-en-general-politeness-multilabel-intel 0.8967
general-politeness-multilabel enguard/medium-guard-128m-xx-general-politeness-multilabel-intel 0.8949
general-politeness-multilabel enguard/tiny-guard-8m-en-general-politeness-multilabel-intel 0.8939
general-politeness-multilabel enguard/tiny-guard-4m-en-general-politeness-multilabel-intel 0.8910
general-politeness-multilabel enguard/tiny-guard-2m-en-general-politeness-multilabel-intel 0.8848
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-general-politeness-binary-intel on huggingface.co

12
Total runs
0
24-hour runs
1
3-day runs
4
7-day runs
5
30-day runs

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