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---
license: apache-2.0
language:
- tr
metrics:
- accuracy
- recall
- f1
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. |