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tiny-guard-2m-en-general-politeness-multiclass-intel

enguard / tiny-guard-2m-en-general-politeness-multiclass-intel

Run on huggingface.co
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Total runs: 16
24-hour runs: 0
7-day runs: 1
30-day runs: 9
Model's Last Updated: November 06 2025
text-classification

Introduction of tiny-guard-2m-en-general-politeness-multiclass-intel

Model Details of tiny-guard-2m-en-general-politeness-multiclass-intel

enguard/tiny-guard-2m-en-general-politeness-multiclass-intel

This model is a fine-tuned Model2Vec classifier based on minishlab/potion-base-2m for the general-politeness-multiclass 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-2m-en-general-politeness-multiclass-intel"
)


# 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 general-politeness-multiclass
Base Model minishlab/potion-base-2m
Precision 0.9875
Recall 0.9704
F1 0.9789
Confusion Matrix
True \ Predicted impolite neutral polite somewhat polite
impolite 2457 36 5 34
neutral 16 2239 80 218
polite 2 104 2177 284
somewhat polite 13 195 235 2105
Full metrics (JSON) 
{
  "impolite": {
    "precision": 0.987540192926045,
    "recall": 0.9703791469194313,
    "f1-score": 0.9788844621513945,
    "support": 2532.0
  },
  "neutral": {
    "precision": 0.8698523698523698,
    "recall": 0.8770074422248335,
    "f1-score": 0.8734152525843574,
    "support": 2553.0
  },
  "polite": {
    "precision": 0.8718462154585502,
    "recall": 0.848071679002727,
    "f1-score": 0.8597946287519748,
    "support": 2567.0
  },
  "somewhat polite": {
    "precision": 0.7970465732677017,
    "recall": 0.826138147566719,
    "f1-score": 0.8113316631335518,
    "support": 2548.0
  },
  "accuracy": 0.8801960784313726,
  "macro avg": {
    "precision": 0.8815713378761667,
    "recall": 0.8803991039284277,
    "f1-score": 0.8808565016553196,
    "support": 10200.0
  },
  "weighted avg": {
    "precision": 0.8813812522049066,
    "recall": 0.8801960784313726,
    "f1-score": 0.8806598517339025,
    "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 0.0002 5622.39
1000 0.0545 18354.69
10000 0.6281 15920.3
Other model variants

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

Classifies Model Precision Recall F1
general-politeness-binary enguard/tiny-guard-2m-en-general-politeness-binary-intel 0.9843 0.9889 0.9866
general-politeness-multiclass enguard/tiny-guard-2m-en-general-politeness-multiclass-intel 0.9875 0.9704 0.9789
general-politeness-binary enguard/tiny-guard-4m-en-general-politeness-binary-intel 0.9831 0.9878 0.9854
general-politeness-multiclass enguard/tiny-guard-4m-en-general-politeness-multiclass-intel 0.9896 0.9783 0.9839
general-politeness-binary enguard/tiny-guard-8m-en-general-politeness-binary-intel 0.9828 0.9905 0.9866
general-politeness-multiclass enguard/tiny-guard-8m-en-general-politeness-multiclass-intel 0.9873 0.9795 0.9833
general-politeness-binary enguard/small-guard-32m-en-general-politeness-binary-intel 0.9858 0.9889 0.9874
general-politeness-multiclass enguard/small-guard-32m-en-general-politeness-multiclass-intel 0.9897 0.9862 0.9879
general-politeness-binary enguard/medium-guard-128m-xx-general-politeness-binary-intel 0.9831 0.9901 0.9866
general-politeness-multiclass enguard/medium-guard-128m-xx-general-politeness-multiclass-intel 0.9881 0.9870 0.9876
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-2m-en-general-politeness-multiclass-intel on huggingface.co

16
Total runs
0
24-hour runs
2
3-day runs
1
7-day runs
9
30-day runs

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