---
license: apache-2.0
widget:
- text: These are nice flowers
- text: What the hell
- text: You really suck, dude
- text: How to put screw thread in furniture? 
- text: The vacuum cleaner began to suck up the dust from the carpet, making the room much cleaner.
metrics:
- name: Accuracy
  type: accuracy
  value: 0.9748
- name: Precision
  type: precision
  value: 0.9331
- name: Recall
  type: recall
  value: 0.9416
- name: F1 Score
  type: f1
  value: 0.9373 
- name: AUC-ROC
  type: roc_auc
  value: 0.9955
base_model: distilbert/distilbert-base-uncased
datasets: 
- tarekziade/profanity
library_name: "transformers"
---

Fine-tuned model that detects profanity in text.

Inspired from https://victorzhou.com/blog/better-profanity-detection-with-scikit-learn/

The model was trained with the dataset from that project.

Usage example with Python:

```
from transformers import pipeline

classifier = pipeline("sentiment-analysis", model="tarekziade/pardonmyai")

print(classifier("These are beautiful flowers"))
```


Usage example with Transformers.js:

```
import { pipeline } from '@xenova/transformers';

let pipe = await pipeline('sentiment-analysis', model='tarekziade/pardonmyai');

let out = await pipe('These are beautiful flowers');
```


Source code and data: https://github.com/tarekziade/pardonmyai

metrics:

- Accuracy: 0.9748
- Precision: 0.9331
- Recall: 0.9416
- F1 Score: 0.9373
- AUC-ROC: 0.9955

There's a tiny version available: https://huggingface.co/tarekziade/pardonmyai-tiny