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--- |
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license: apache-2.0 |
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--- |
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## Deprem Niyet Sınıflandırma (Dataset v1.3, BERT 128k) |
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Alakasız sınıfı atılarak eğitildi. |
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## Eval Results |
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``` |
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precision recall f1-score support |
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Lojistik 0.83 0.86 0.84 22 |
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Elektrik Kaynagi 0.71 0.95 0.81 39 |
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Arama Ekipmani 0.72 0.80 0.76 82 |
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Cenaze 0.50 0.33 0.40 3 |
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Giysi 0.79 0.96 0.87 91 |
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Enkaz Kaldirma 0.99 0.95 0.97 601 |
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Isinma 0.75 0.90 0.82 112 |
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Barınma 0.98 0.95 0.96 292 |
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Tuvalet 0.83 1.00 0.91 5 |
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Su 0.80 0.85 0.83 39 |
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Yemek 0.94 0.95 0.94 138 |
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Saglik 0.80 0.85 0.83 75 |
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micro avg 0.90 0.93 0.92 1499 |
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macro avg 0.80 0.86 0.83 1499 |
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weighted avg 0.91 0.93 0.92 1499 |
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samples avg 0.94 0.95 0.94 1499 |
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``` |
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Reproducibility icin trainer arg'lari: |
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```python |
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TrainingArguments( |
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fp16=True, |
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evaluation_strategy = "steps", |
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save_strategy = "steps", |
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learning_rate=5.1058553791201954e-05, |
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per_device_train_batch_size=batch_size, |
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per_device_eval_batch_size=batch_size*2, |
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num_train_epochs=4, |
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load_best_model_at_end=True, |
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metric_for_best_model="macro f1", |
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logging_steps = step_size, |
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seed = 42, |
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data_seed = 42, |
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dataloader_num_workers = 0, |
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lr_scheduler_type ="linear", |
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warmup_steps=0, |
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weight_decay=0.06437697487126866, |
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full_determinism = True, |
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group_by_length = True |
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) |
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``` |
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Threshold: |
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Best Threshold: 0.52 |
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