
## 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;

|        | Cinsiyetçilik | Irkçılık | Kızdırma | Nötr | 
| ------ | ------  | ------ | ------  | ------ |
| Precision | 0.925 | 0.878 | 0.824 | 0.915 |
| Recall  | 0.831 | 0.896 | 0.843 | 0.935 |
| F1 Score | 0.875 | 0.887 | 0.833 | 0.925 |
Accuracy : 0.886

## Example
```sh
from transformers import AutoTokenizer, TextClassificationPipeline, TFBertForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("nanelimon/bert-base-turkish-bullying")
model = TFBertForSequenceClassification.from_pretrained("nanelimon/bert-base-turkish-bullying", from_pt=True)
pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer)

print(pipe('Bu bir denemedir hadi sende dene!'))
```
Result;
```sh
[{'label': 'Nötr', 'score': 0.999175488948822}]
```
- 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
- Elif SARIGIL KARA: elifsarigil@gmail.com
- Murat KOKLU: mkoklu@selcuk.edu.tr
- Alaaddin Erdinç DAL: aerdincdal@icloud.com


## License

gpl-3.0

**Free Software, Hell Yeah!**
{"language": ["tr"], "license": "mit", "tags": ["translation"]}
transformers pytorch bert text-classification translation tr license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #translation #tr #license-mit #autotrain_compatible #endpoints_compatible #region-us 

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;



Example
-------


Result;


* 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@URL
* Elif SARIGIL KARA: elifsarigil@URL
* Murat KOKLU: mkoklu@URL
* Alaaddin Erdinç DAL: aerdincdal@URL


License
-------


gpl-3.0


Free Software, Hell Yeah!



TAGS
#transformers #pytorch #bert #text-classification #translation #tr #license-mit #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #bert #text-classification #translation #tr #license-mit #autotrain_compatible #endpoints_compatible #region-us 
