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README.md
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A paper on this model is being released soon.
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## Benchmarks
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The Tiny-Toxic-Detector achieves an impressive 90.26% on the Toxigen benchmark and 87.34% on the Jigsaw-Toxic-Comment-Classification-Challenge. Here we compare our results against other toxic classification models:
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| Model | Size (parameters) | Toxigen (%) | Jigsaw (%) |
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| lmsys/toxicchat-t5-large-v1.0 | 738M | 72.67 | 88.82 |
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| s-nlp/roberta toxicity classifier | 124M | 88.41 | **94.92** |
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| mohsenfayyaz/toxicity-classifier | 109M | 81.50 | 83.31 |
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| martin-ha/toxic-comment-model | 67M | 68.02 | 91.56 |
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| **Tiny-toxic-detector** | **2M** | **90.97** | 86.98 |
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## Usage
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This model uses custom architecture and requires some extra custom code to work. Below you can find the architecture and a fully-usable example.
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prediction = "Toxic" if logits > 0.5 else "Not Toxic"
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```
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## Usage and Limitations
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A paper on this model is being released soon.
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## Usage
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This model uses custom architecture and requires some extra custom code to work. Below you can find the architecture and a fully-usable example.
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prediction = "Toxic" if logits > 0.5 else "Not Toxic"
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```
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## Benchmarks
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The Tiny-Toxic-Detector achieves an impressive 90.26% on the Toxigen benchmark and 87.34% on the Jigsaw-Toxic-Comment-Classification-Challenge. Here we compare our results against other toxic classification models:
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| Model | Size (parameters) | Toxigen (%) | Jigsaw (%) |
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| --------------------------------- | ----------------- | ----------- | ---------- |
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| lmsys/toxicchat-t5-large-v1.0 | 738M | 72.67 | 88.82 |
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| s-nlp/roberta toxicity classifier | 124M | 88.41 | **94.92** |
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| mohsenfayyaz/toxicity-classifier | 109M | 81.50 | 83.31 |
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| martin-ha/toxic-comment-model | 67M | 68.02 | 91.56 |
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| **Tiny-toxic-detector** | **2M** | **90.97** | 86.98 |
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## Usage and Limitations
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