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tiny-guard-4m-en-general-politeness-multilabel-intel

enguard / tiny-guard-4m-en-general-politeness-multilabel-intel

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Total runs: 31
24-hour runs: 0
7-day runs: 31
30-day runs: 31
Model's Last Updated: November 03 2025
text-classification

Introduction of tiny-guard-4m-en-general-politeness-multilabel-intel

Model Details of tiny-guard-4m-en-general-politeness-multilabel-intel

enguard/tiny-guard-4m-en-general-politeness-multilabel-intel

This model is a fine-tuned Model2Vec classifier based on minishlab/potion-base-4m for the general-politeness-multilabel found in the Intel/polite-guard dataset.

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

model = StaticModelPipeline.from_pretrained(
  "enguard/tiny-guard-4m-en-general-politeness-multilabel-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-multilabel
Base Model minishlab/potion-base-4m
F1 0.8910
Full metrics (JSON) 
{
  "impolite": {
    "precision": 0.9804763764154627,
    "recall": 0.9917061611374408,
    "f1-score": 0.9860592970744159,
    "support": 2532.0
  },
  "neutral": {
    "precision": 0.8867998433215825,
    "recall": 0.8867998433215825,
    "f1-score": 0.8867998433215825,
    "support": 2553.0
  },
  "polite": {
    "precision": 0.8922131147540984,
    "recall": 0.848071679002727,
    "f1-score": 0.8695825843818654,
    "support": 2567.0
  },
  "somewhat polite": {
    "precision": 0.8072562358276644,
    "recall": 0.8383045525902669,
    "f1-score": 0.8224874855602619,
    "support": 2548.0
  },
  "accuracy": 0.8909803921568628,
  "macro avg": {
    "precision": 0.891686392579702,
    "recall": 0.8912205590130042,
    "f1-score": 0.8912323025845315,
    "support": 10200.0
  },
  "weighted avg": {
    "precision": 0.8915456999555501,
    "recall": 0.8909803921568628,
    "f1-score": 0.8910410536772762,
    "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. somewhat polite somewhat polite
I understand you're concerned about the ski lessons, and I'll look into the options for rescheduling. somewhat polite somewhat polite
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. neutral neutral
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. neutral neutral
I'll look into your policy details and see what options are available to you. somewhat polite somewhat polite
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. somewhat polite somewhat polite
Prediction Speed Benchmarks
Dataset Size Time (seconds) Predictions/Second
1 0.0003 3788.89
1000 0.0254 39392.01
10000 0.3027 33033.82
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 tiny-guard-4m-en-general-politeness-multilabel-intel on huggingface.co

31
Total runs
0
24-hour runs
16
3-day runs
31
7-day runs
31
30-day runs

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