Update README.md

README.md

@@ -19,7 +19,7 @@ should probably proofread and complete it, then remove this comment. -->
 
 # bert-ner-turkish-cased
 
-This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on the a custom Turkish NER dataset.
+This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on a custom Turkish NER dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.0987
 - Precision: 0.9112
@@ -37,14 +37,43 @@ LABELS = [
     "B-DATE", "I-DATE", "B-MONEY", "I-MONEY", "B-MISC", "I-MISC"
 ]
 ```
+- PER: Person
+- LOC: Location
+- ORG: Organization
+- DATE: Date
+- MONEY: Money
+- MISC: Miscellaneous Entities
 
 ## Intended uses & limitations
 
-More information needed
+Extracting entities from Turkish text in NLP pipelines.
 
-## Training and evaluation data
+## How to Use
+
+```python
+from transformers import pipeline
+
+model_name = "yeniguno/bert-ner-turkish-cased"
+
+ner_pipeline = pipeline("ner", model=model_name, tokenizer=model_name, aggregation_strategy="simple")
+
+text = """Selim Parlak, 2023-11-15 tarihinde, CUMHURİYET MAH. DUMAN SOKAK 22500 HAVSA/EDİRNE adresinden, Dünya Varlık Yönetim A.Ş. aracılığıyla 850 TRY değerindeki MP.2386.JPA.IP5.WHT.I İPHONE5 ŞARJLI KILIF "AİR" 1700 MAH (BEYAZ) ürününü satın aldı."""
+
+results = ner_pipeline(text)
+
+for result in results:
+    print(result)
+
+"""
+{'entity_group': 'PER', 'score': 0.9993254, 'word': 'Selim Parlak', 'start': 0, 'end': 12}
+{'entity_group': 'DATE', 'score': 0.9987677, 'word': '2023 - 11 - 15', 'start': 14, 'end': 24}
+{'entity_group': 'LOC', 'score': 0.99951524, 'word': 'CUMHURİYET MAH. DUMAN SOKAK 22500 HAVSA / EDİRNE', 'start': 36, 'end': 82}
+{'entity_group': 'ORG', 'score': 0.8487069, 'word': 'Dünya Varlık Yönetim A. Ş.', 'start': 95, 'end': 120}
+{'entity_group': 'MONEY', 'score': 0.9970985, 'word': '850 TRY', 'start': 134, 'end': 141}
+{'entity_group': 'MISC', 'score': 0.97721404, 'word': 'MP. 2386. JPA. IP5. WHT. I İPHONE5 ŞARJLI KILIF " AİR " 1700 MAH ( BEYAZ )', 'start': 154, 'end': 219}
+"""
+```
 
-More information needed
 
 ## Training procedure