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turkish_hate_speech

PEFT
Safetensors
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metadata
library_name: peft
license: apache-2.0
base_model: google-bert/bert-base-multilingual-cased
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: turkish_hate_speech
    results: []

turkish_hate_speech

This model is a fine-tuned version of google-bert/bert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3413
  • Accuracy: 0.8566
  • F1 Macro: 0.8564
  • Precision Macro: 0.8612
  • Recall Macro: 0.8580
  • F1 Nefret: 0.8512
  • F1 Hicbiri: 0.8615

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: 0.0001
  • train_batch_size: 8
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.01
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Macro Precision Macro Recall Macro F1 Nefret F1 Hicbiri
0.5934 1.0 800 0.4766 0.7694 0.7670 0.7818 0.7697 0.7432 0.7907
0.4308 2.0 1600 0.3973 0.8184 0.8184 0.8186 0.8184 0.8212 0.8156
0.3898 3.0 2400 0.3548 0.8431 0.8430 0.8440 0.8432 0.8396 0.8465
0.3393 4.0 3200 0.3355 0.8538 0.8535 0.8566 0.8539 0.8474 0.8596
0.319 5.0 4000 0.3220 0.86 0.8600 0.8601 0.8600 0.8590 0.8610
0.3053 6.0 4800 0.3201 0.8641 0.8640 0.8654 0.8642 0.8603 0.8677
0.2887 7.0 5600 0.3166 0.8638 0.8634 0.8673 0.8639 0.8570 0.8699
0.2908 8.0 6400 0.3271 0.8634 0.8631 0.8679 0.8636 0.8559 0.8702
0.2764 9.0 7200 0.3207 0.8659 0.8657 0.8692 0.8661 0.8597 0.8717
0.2754 10.0 8000 0.3207 0.8656 0.8654 0.8689 0.8658 0.8594 0.8713

Framework versions

  • PEFT 0.15.1
  • Transformers 4.50.3
  • Pytorch 2.5.1+cu121
  • Datasets 3.5.0
  • Tokenizers 0.21.0