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Replying to Avatar Rif'at Ahdi R

In the last few days, I have some experiments with traditional #MachineLearning algorithms for toxicity classification. Instead of using GPU, the trained model can be utilized only using CPU with fast performance (milliseconds). Good news for those who can't afford GPU.

The performance result is satisfactory and comparable to top state of the art model such as "detoxify/unbiased-small" model. This model will be included for #NostrFilterRelay module https://nostrcheck.me/media/2b67e480b7f99d2835684a8f7276d86edbe8e318ea55cf77ccfd559c5f24f645/d8a67688414deaccc639628633a2c5793cf96949dc16301e113e9678172d889a.webp next update.

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Rif'at Ahdi R 1y ago

Update:

Jupyter Notebook example for training has been merged in main branch including pre-trained model. The model will be integrated into API as additional option in the next update.

https://nostrcheck.me/media/2b67e480b7f99d2835684a8f7276d86edbe8e318ea55cf77ccfd559c5f24f645/8082b580b7765498b93b7f0c3f9c92f7cb4ec7cd59cf90621b3a12d03243ca73.webp

https://github.com/atrifat/hate-speech-detector-api/blob/main/experiments/hate-speech-classification.ipynb

nostr:nevent1qqsv6vwwy85fsz4rpa9z7k8hkvsvsv60vsj3q987jzqdstsznpjqxgqpzfmhxue69uhkuenjv4kxz7fwv9c8qtczyq4k0eyqklue62p4dp9g7unkmphdh68rrr49tnmhen74t8zlynmy2qcyqqqqqqgm0c5yu

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