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--- |
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license: apache-2.0 |
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widget: |
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- text: These are nice flowers |
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- text: What the hell |
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- text: You really suck, dude |
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- text: How to put screw thread in furniture? |
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- text: The vacuum cleaner began to suck up the dust from the carpet, making the room much cleaner. |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9748 |
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- name: Precision |
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type: precision |
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value: 0.9331 |
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- name: Recall |
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type: recall |
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value: 0.9416 |
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- name: F1 Score |
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type: f1 |
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value: 0.9373 |
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- name: AUC-ROC |
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type: roc_auc |
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value: 0.9955 |
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base_model: distilbert/distilbert-base-uncased |
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datasets: |
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- tarekziade/profanity |
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library_name: "transformers" |
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--- |
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Fine-tuned model that detects profanity in text. |
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Inspired from https://victorzhou.com/blog/better-profanity-detection-with-scikit-learn/ |
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The model was trained with the dataset from that project. |
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Usage example with Python: |
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``` |
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from transformers import pipeline |
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classifier = pipeline("sentiment-analysis", model="tarekziade/pardonmyai") |
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print(classifier("These are beautiful flowers")) |
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``` |
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Usage example with Transformers.js: |
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``` |
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import { pipeline } from '@xenova/transformers'; |
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let pipe = await pipeline('sentiment-analysis', model='tarekziade/pardonmyai'); |
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let out = await pipe('These are beautiful flowers'); |
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``` |
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Source code and data: https://github.com/tarekziade/pardonmyai |
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metrics: |
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- Accuracy: 0.9748 |
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- Precision: 0.9331 |
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- Recall: 0.9416 |
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- F1 Score: 0.9373 |
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- AUC-ROC: 0.9955 |
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There's a tiny version available: https://huggingface.co/tarekziade/pardonmyai-tiny |
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