---
license: apache-2.0
language:
  - tr
metrics:
  - accuracy
  - recall
  - f1
tags:
  - deprem-clf-v1
library_name: transformers
pipeline_tag: text-classification
model-index:
  - name: deprem_v12
    results:
      - task:
          type: text-classification
        dataset:
          type: deprem_private_dataset_v1_2
          name: deprem_private_dataset_v1_2
        metrics:
          - type: recall
            value: 0.82
            verified: false
          - type: f1
            value: 0.76
            verified: false
widget:
  - text: >-
      acil acil acil antakyadan istanbula gitmek için antakya expoya ulaşmaya çalışan 19 kişilik bir aile için şehir içi ulaşım desteği istiyoruz. dışardalar üşüyorlar.iletebileceğiniz numaraları bekliyorum 
    example_title: Örnek
---



## Eval Results
```
              precision    recall  f1-score   support

    Alakasiz       0.87      0.91      0.89       734
     Barinma       0.79      0.89      0.84       207
  Elektronik       0.69      0.83      0.75       130
       Giysi       0.71      0.81      0.76        94
    Kurtarma       0.82      0.85      0.83       362
    Lojistik       0.57      0.67      0.62       112
      Saglik       0.68      0.85      0.75       108
          Su       0.56      0.76      0.64        78
       Yagma       0.60      0.77      0.68        31
       Yemek       0.71      0.89      0.79       117

   micro avg       0.77      0.86      0.81      1973
   macro avg       0.70      0.82      0.76      1973
weighted avg       0.78      0.86      0.82      1973
 samples avg       0.83      0.88      0.84      1973


```

## Training Params:
```python
{'per_device_train_batch_size': 32,
 'per_device_eval_batch_size': 32,
 'learning_rate': 5.8679699888213376e-05,
 'weight_decay': 0.03530961718117487,
 'num_train_epochs': 4,
 'lr_scheduler_type': 'cosine',
 'warmup_steps': 40,
 'seed': 42,
 'fp16': True,
 'load_best_model_at_end': True,
 'metric_for_best_model': 'macro f1',
 'greater_is_better': True
}
```

## Threshold:
- **Best Threshold:** 0.40

## Class Loss Weights
- Same as Anıl's approach:
```python
    [1.0,
     1.5167249178108022,
     1.7547338578655642,
     1.9610520059358458,
     1.8684086209021484,
     1.8019018017117145,
     2.110648663094536,
     3.081208739200435,
     1.7994815143101963]

```