Update README

README.md

@@ -1,5 +1,74 @@
 ---
 license: apache-2.0
+language:
+- tr
+pipeline_tag: text-classification
+tags:
+- job advertisement
+- turkish bert
+- bert-based
+- StratifiedKFold
 ---
 
-**It is not yet ready to serve. We are working on it.**
+---
+language:
+- tr
+tags:
+- translation
+license: apache-2.0
+---
+
+## About the model
+It has been trained with 15451 real job advertisement data taken as tagged by isinolsun.com
+
+Included classes;
+
+- Uygun İlan
+- Is Ilani Degil
+- Mustehcen
+- Cift Pozisyon
+
+
+Accordingly, the success rates in education are as follows;
+
+- **Model is Turkish bert-based.**
+- **Used StratifiedKFold(5) for validation.**
+- results [0.806858621805241, 0.8912621359223301, 0.9440129449838188, 0.9750809061488673, 0.9851132686084142]
+  
+Mean-Precision: 0.9204655754937342
+
+
+|        | Uygun İlan | Is Ilani Degil | Mustehcen | Cift Pozisyon | 
+| ------ | ------  | ------ | ------  | ------ |
+| Precision | 0.986 | 0.996 | 0.966 | 0.970 |
+| Recall  | 0.992 | 0.986 | 0.966 | 0.959 |
+| F1 Score | 0.989 | 0.991 | 0.966 | 0.965 |
+Accuracy : 0.975
+
+## Example
+```sh
+from transformers import AutoTokenizer, TextClassificationPipeline, TFBertForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("nanelimon/bert-base-turkish-job-advertisement")
+model = TFBertForSequenceClassification.from_pretrained("nanelimon/bert-base-turkish-job-advertisement", from_pt=True)
+pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer)
+
+print(pipe('Bu bir denemedir hadi sende dene!'))
+```
+Result;
+```sh
+[{'label': 'Is Ilani Degil', 'score': 0.999987899677558}]
+```
+- label= It shows which class the sent Turkish text belongs to according to the model.
+- score= It shows the compliance rate of the Turkish text sent to the label found.
+
+## Authors
+- Seyma SARIGIL: seymasargil@gmail.com
+- Murat KOKLU: mkoklu@selcuk.edu.tr
+
+
+## License
+
+apache-2.0
+
+**Free Software, Hell Yeah!**