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roberta-tagalog-base-philippine-elections-2016-2022-hate-speech

This model is a fine-tuned version of jcblaise/roberta-tagalog-base for the task of Text Classification, classifying hate and non-hate tweets.

The model was fine-tuned on a combined dataset mapsoriano/2016_2022_hate_speech_filipino consisting of the hate_speech_filipino dataset and a newly crawled 2022 Philippine Presidential Elections-related Tweets Hate Speech Dataset.

It achieves the following results on the evaluation (validation) set:

  • Loss: 0.3574
  • Accuracy: 0.8743

It achieves the following results on the test set:

  • Accuracy: 0.8783
  • Precision: 0.8563
  • Recall: 0.9077
  • F1: 0.8813

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 2

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.3423 1.0 1361 0.3167 0.8693
0.2194 2.0 2722 0.3574 0.8743

Framework versions

  • Transformers 4.33.2
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.13.3
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Datasets used to train mapsoriano/roberta-tagalog-base-philippine-elections-2016-2022-hate-speech