Update README.md

README.md

@@ -5,4 +5,58 @@ datasets:
 language:
 - tr
 pipeline_tag: text-classification
----
+---
+
+## About the model
+It is a Turkish bert-based model created to determine the types of bullying that people use against each other in social media.
+Included classes;
+
+- Nötr
+- Kızdırma/Hakaret
+- Cinsiyetçilik
+- Irkçılık
+
+3388 tweets were used in the training of the model. Accordingly, the success rates in education are as follows;
+
+|        | INSULT | OTHER | PROFANITY | RACIST | SEXIST |
+| ------ | ------  | ------ | ------  | ------ | ------ |
+| Precision | 0.901 | 0.924 | 0.978 | 1.000 | 0.980 |
+| Recall  | 0.920 | 0.980 | 0.900 | 0.980 | 1.000 |
+| F1 Score | 0.910 | 0.9514 | 0.937 | 0.989 | 0.990 |
+
+F-Score:  0.9559690799177005
+Recall:  0.9559999999999998
+Precision:  0.9570284225256961
+Accuracy:  0.956
+
+## Dependency
+pip install torch torchvision torchaudio
+pip install tf-keras  
+pip install transformers  
+pip install tensorflow
+
+## Example
+```sh
+from transformers import AutoTokenizer, TextClassificationPipeline, TFBertForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("nanelimon/bert-base-turkish-offensive")
+model = TFBertForSequenceClassification.from_pretrained("nanelimon/bert-base-turkish-offensive", from_pt=True)
+pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True, top_k=2)
+
+print(pipe('Bu bir denemedir hadi sende dene!'))
+```
+Result;
+```sh
+[[{'label': 'OTHER', 'score': 1.000}, {'label': 'INSULT', 'score': 0.000}]]
+```
+- 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: [email protected]
+
+## License
+
+gpl-3.0
+
+**Free Software, Hell Yeah!**