
## Eval Results
```
                  precision    recall  f1-score   support
        Lojistik       0.81      0.79      0.80        38
Elektrik Kaynagi       0.73      0.91      0.81        56
  Arama Ekipmani       0.79      0.74      0.76       128
          Cenaze       1.00      0.50      0.67         2
           Giysi       0.82      0.96      0.89       138
  Enkaz Kaldirma       0.94      0.94      0.94       919
          Isinma       0.84      0.89      0.86       185
         Barınma       0.96      0.96      0.96       483
         Tuvalet       0.67      0.80      0.73        10
              Su       0.83      0.87      0.85        67
           Yemek       0.89      0.96      0.92       202
          Saglik       0.80      0.88      0.83       104
        Alakasiz       0.90      0.82      0.86       377

       micro avg       0.89      0.91      0.90      2709
       macro avg       0.84      0.85      0.84      2709
    weighted avg       0.90      0.91      0.90      2709
     samples avg       0.91      0.92      0.91      2709
```

## Threshold:
- **Best Threshold:** 0.53

## Class Loss Weights

```python
    [3.017203135650159,
     2.4823691788825464,
     1.941736822154725,
     6.172646581418988,
     1.8759436445637834,
     1.0,
     1.75011143349181,
     1.2730236191357969,
     4.849237178079731,
     2.4857419672410703,
     1.6324480531290084,
     2.0033774839735035,
     1.3688883733394182]
```
{"language": ["tr"], "license": "apache-2.0", "library_name": "transformers", "tags": ["deprem-clf-v13"], "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 9 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_v13", "results": [{"task": {"type": "text-classification"}, "dataset": {"name": "deprem_private_dataset_v13", "type": "deprem_private_dataset_v13"}, "metrics": [{"type": "recall", "value": 0.85, "verified": false}, {"type": "f1", "value": 0.84, "verified": false}]}]}]}
transformers pytorch bert text-classification deprem-clf-v13 tr license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #deprem-clf-v13 #tr #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 


## Eval Results


## Threshold:
- Best Threshold: 0.53

## Class Loss Weights


## Eval Results ## Threshold:
- Best Threshold: 0.53 ## Class Loss Weights
TAGS
#transformers #pytorch #bert #text-classification #deprem-clf-v13 #tr #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ## Eval Results ## Threshold:
- Best Threshold: 0.53 ## Class Loss Weights
TAGS
#transformers #pytorch #bert #text-classification #deprem-clf-v13 #tr #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
## Eval Results## Threshold:
- Best Threshold: 0.53## Class Loss Weights