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--- |
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library_name: peft |
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license: apache-2.0 |
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base_model: google-bert/bert-base-multilingual-cased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: turkish_hate_speech |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# turkish_hate_speech |
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This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3413 |
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- Accuracy: 0.8566 |
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- F1 Macro: 0.8564 |
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- Precision Macro: 0.8612 |
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- Recall Macro: 0.8580 |
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- F1 Nefret: 0.8512 |
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- F1 Hicbiri: 0.8615 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 8 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.01 |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Precision Macro | Recall Macro | F1 Nefret | F1 Hicbiri | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|:---------:|:----------:| |
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| 0.5934 | 1.0 | 800 | 0.4766 | 0.7694 | 0.7670 | 0.7818 | 0.7697 | 0.7432 | 0.7907 | |
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| 0.4308 | 2.0 | 1600 | 0.3973 | 0.8184 | 0.8184 | 0.8186 | 0.8184 | 0.8212 | 0.8156 | |
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| 0.3898 | 3.0 | 2400 | 0.3548 | 0.8431 | 0.8430 | 0.8440 | 0.8432 | 0.8396 | 0.8465 | |
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| 0.3393 | 4.0 | 3200 | 0.3355 | 0.8538 | 0.8535 | 0.8566 | 0.8539 | 0.8474 | 0.8596 | |
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| 0.319 | 5.0 | 4000 | 0.3220 | 0.86 | 0.8600 | 0.8601 | 0.8600 | 0.8590 | 0.8610 | |
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| 0.3053 | 6.0 | 4800 | 0.3201 | 0.8641 | 0.8640 | 0.8654 | 0.8642 | 0.8603 | 0.8677 | |
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| 0.2887 | 7.0 | 5600 | 0.3166 | 0.8638 | 0.8634 | 0.8673 | 0.8639 | 0.8570 | 0.8699 | |
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| 0.2908 | 8.0 | 6400 | 0.3271 | 0.8634 | 0.8631 | 0.8679 | 0.8636 | 0.8559 | 0.8702 | |
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| 0.2764 | 9.0 | 7200 | 0.3207 | 0.8659 | 0.8657 | 0.8692 | 0.8661 | 0.8597 | 0.8717 | |
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| 0.2754 | 10.0 | 8000 | 0.3207 | 0.8656 | 0.8654 | 0.8689 | 0.8658 | 0.8594 | 0.8713 | |
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### Framework versions |
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- PEFT 0.15.1 |
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- Transformers 4.50.3 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.5.0 |
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- Tokenizers 0.21.0 |
