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---
base_model: minishlab/potion-base-2m
datasets:
- Intel/polite-guard
library_name: model2vec
license: mit
model_name: enguard/tiny-guard-2m-en-general-politeness-binary-intel
tags:
- static-embeddings
- text-classification
- model2vec
---
# enguard/tiny-guard-2m-en-general-politeness-binary-intel
This model is a fine-tuned Model2Vec classifier based on [minishlab/potion-base-2m](https://huggingface.co/minishlab/potion-base-2m) for the general-politeness-binary found in the [Intel/polite-guard](https://huggingface.co/datasets/Intel/polite-guard) dataset.
## Installation
```bash
pip install model2vec[inference]
```
## Usage
```python
from model2vec.inference import StaticModelPipeline
model = StaticModelPipeline.from_pretrained(
"enguard/tiny-guard-2m-en-general-politeness-binary-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-binary |
| Base Model | [minishlab/potion-base-2m](https://huggingface.co/minishlab/potion-base-2m) |
| Precision | 0.9843 |
| Recall | 0.9889 |
| F1 | 0.9866 |
### Confusion Matrix
| True \ Predicted | FAIL | PASS |
| --- | --- | --- |
| **FAIL** | 2504 | 28 |
| **PASS** | 40 | 7628 |
<details>
<summary><b>Full metrics (JSON)</b></summary>
```json
{
"FAIL": {
"precision": 0.9842767295597484,
"recall": 0.9889415481832543,
"f1-score": 0.9866036249014972,
"support": 2532.0
},
"PASS": {
"precision": 0.9963427377220481,
"recall": 0.9947835159102765,
"f1-score": 0.9955625163142783,
"support": 7668.0
},
"accuracy": 0.9933333333333333,
"macro avg": {
"precision": 0.9903097336408982,
"recall": 0.9918625320467653,
"f1-score": 0.9910830706078877,
"support": 10200.0
},
"weighted avg": {
"precision": 0.9933475286370538,
"recall": 0.9933333333333333,
"f1-score": 0.9933386032694584,
"support": 10200.0
}
}
```
</details>
<details>
<summary><b>Sample Predictions</b></summary>
| 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 |
</details>
<details>
<summary><b>Prediction Speed Benchmarks</b></summary>
| Dataset Size | Time (seconds) | Predictions/Second |
|--------------|----------------|---------------------|
| 1 | 0.0002 | 5108.77 |
| 1000 | 0.0542 | 18439.74 |
| 10000 | 0.6208 | 16108.79 |
</details>
## 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](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/enguard/medium-guard-128m-xx-general-politeness-multiclass-intel) | 0.9881 | 0.9870 | 0.9876 |
## Resources
- Awesome AI Guardrails: <https://github.com/enguard-ai/awesome-ai-guardails>
- Model2Vec: https://github.com/MinishLab/model2vec
- Docs: https://minish.ai/packages/model2vec/introduction
## 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}
}
``` |