Update README.md

README.md

@@ -23,6 +23,8 @@ model-index:
     - name: F1
       type: f1
       value: 0.7821495486288537
+language:
+- tr
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -37,16 +39,58 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
+This model is a fine-tuned version of dbmdz/bert-base-turkish-uncased on the turkish-wiki_ner dataset. The training dataset consists of 18,967 samples, and the validation dataset consists of 1,000 samples, both derived from Wikipedia data.
+
+
+For more detailed information, please visit this link: https://huggingface.co/datasets/turkish-nlp-suite/turkish-wikiNER
+-
+Labels:
+
+<ul>
+  <li>CARDINAL</li>
+  <li>DATE</li>
+  <li>EVENT</li>
+  <li>FAC</li>
+  <li>GPE</li>
+  <li>LANGUAGE</li>
+  <li>LAW</li>
+  <li>LOC</li>
+  <li>MONEY</li>
+  <li>NORP</li>
+  <li>ORDINAL</li>
+  <li>ORG</li>
+  <li>PERCENT</li>
+  <li>PERSON</li>
+  <li>PRODUCT</li>
+  <li>QUANTITY</li>
+  <li>TIME</li>
+  <li>TITLE</li>
+  <li>WORK_OF_ART</li>
+</ul>
+
+Fine-Tuning Process : https://github.com/saribasmetehan/bert-base-turkish-uncased-ner
+-
+## Example
+```markdown
+from transformers import pipeline
+text = "Bu toplam sıfır ise, Newton'ın birinci yasası cismin hareket durumunun değişmeyeceğini söyler."
+model_id = saribasmetehan/bert-base-turkish-uncased-ner"
+ner = pipeline("ner",model = model_id)
+preds= ner(text, aggregation_strategy = "simple")
+
+pd.DataFrame(preds)
+
+```
+
+## Load model directly
+```markdown
+from transformers import AutoModelForTokenClassification, AutoTokenizer
+
+model_name = "saribasmetehan/bert-base-turkish-uncased-ner"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForTokenClassification.from_pretrained(model_name)
+
+```
 ## Training procedure
 
 ### Training hyperparameters
@@ -75,4 +119,4 @@ The following hyperparameters were used during training:
 - Transformers 4.41.2
 - Pytorch 2.3.0+cu121
 - Datasets 2.19.2
-- Tokenizers 0.19.1
+- Tokenizers 0.19.1