


## 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]

```
{"language": ["tr"], "license": "apache-2.0", "library_name": "transformers", "tags": ["deprem-clf-v1"], "metrics": ["accuracy", "recall", "f1"], "pipeline_tag": "text-classification", "widget": [{"text": "acil acil acil antakyadan istanbula gitmek i\u00e7in antakya expoya ula\u015fmaya \u00e7al\u0131\u015fan 19 ki\u015filik bir aile i\u00e7in \u015fehir i\u00e7i ula\u015f\u0131m deste\u011fi istiyoruz. d\u0131\u015fardalar \u00fc\u015f\u00fcyorlar.iletebilece\u011finiz numaralar\u0131 bekliyorum ", "example_title": "\u00d6rnek"}], "model-index": [{"name": "deprem_v12", "results": [{"task": {"type": "text-classification"}, "dataset": {"name": "deprem_private_dataset_v1_2", "type": "deprem_private_dataset_v1_2"}, "metrics": [{"type": "recall", "value": 0.82, "verified": false}, {"type": "f1", "value": 0.76, "verified": false}]}]}]}
transformers pytorch bert text-classification deprem-clf-v1 tr license:apache-2.0 model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #text-classification #deprem-clf-v1 #tr #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 




## Eval Results


## Training Params:


## Threshold:
- Best Threshold: 0.40

## Class Loss Weights
- Same as Anıl's approach:

## Eval Results ## Training Params: ## Threshold:
- Best Threshold: 0.40 ## Class Loss Weights
- Same as Anıl's approach:
TAGS
#transformers #pytorch #bert #text-classification #deprem-clf-v1 #tr #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ## Eval Results ## Training Params: ## Threshold:
- Best Threshold: 0.40 ## Class Loss Weights
- Same as Anıl's approach:
TAGS
#transformers #pytorch #bert #text-classification #deprem-clf-v1 #tr #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
## Eval Results## Training Params:## Threshold:
- Best Threshold: 0.40## Class Loss Weights
- Same as Anıl's approach: