Update README.md

README.md

@@ -1,3 +1,53 @@
 ---
 license: apache-2.0
 ---
+## Deprem Niyet Sınıflandırma (Dataset v1.3, BERT 128k)
+Alakasız sınıfı atılarak eğitildi. 
+## Eval Results
+```
+                 precision    recall  f1-score   support
+
+        Lojistik       0.83      0.86      0.84        22
+Elektrik Kaynagi       0.71      0.95      0.81        39
+  Arama Ekipmani       0.72      0.80      0.76        82
+          Cenaze       0.50      0.33      0.40         3
+           Giysi       0.79      0.96      0.87        91
+  Enkaz Kaldirma       0.99      0.95      0.97       601
+          Isinma       0.75      0.90      0.82       112
+         Barınma       0.98      0.95      0.96       292
+         Tuvalet       0.83      1.00      0.91         5
+              Su       0.80      0.85      0.83        39
+           Yemek       0.94      0.95      0.94       138
+          Saglik       0.80      0.85      0.83        75
+
+       micro avg       0.90      0.93      0.92      1499
+       macro avg       0.80      0.86      0.83      1499
+    weighted avg       0.91      0.93      0.92      1499
+     samples avg       0.94      0.95      0.94      1499
+
+```
+Reproducibility icin trainer arg'lari:
+```python
+TrainingArguments(
+    fp16=True,
+    evaluation_strategy = "steps",
+    save_strategy = "steps",
+    learning_rate=5.1058553791201954e-05,
+    per_device_train_batch_size=batch_size,
+    per_device_eval_batch_size=batch_size*2,
+    num_train_epochs=4,
+    load_best_model_at_end=True,
+    metric_for_best_model="macro f1",
+    logging_steps = step_size,
+    seed = 42,
+    data_seed = 42,
+    dataloader_num_workers = 0,
+    lr_scheduler_type ="linear",
+    warmup_steps=0,  
+    weight_decay=0.06437697487126866,  
+    full_determinism = True,
+    group_by_length = True
+)
+```
+Threshold:
+Best Threshold: 0.52