roberta-tagalog-profanity-classifier

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roberta-tagalog-profanity-classifier

This model is a fine-tuned version of jcblaise/roberta-tagalog-base on mginoben/tagalog-profanity-dataset dataset. It achieves the following results on the evaluation set:

• Loss: 0.3019
• Accuracy: 0.8898
• Precision: 0.8523
• Recall: 0.8944
• F1: 0.8728

Model description

The Model classifies tagalog texts that contains profanities as either Abusive or Non-Abusive.

It only classifies texts with the following profanities:

• bobo
• bwiset
• gago
• kupal
• pakshet
• pakyu
• pucha
• punyeta
• puta
• putangina
• tanga
• tangina
• tarantado
• ulol

Intended uses & limitations

For content moderation accross different social medias

Training and evaluation data

• Training: 11,110
• Validation: 2,778

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e-05
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Framework versions

• Transformers 4.28.0
• Pytorch 2.0.1+cu118
• Datasets 2.12.0
• Tokenizers 0.13.3