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--- |
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language: |
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- tr |
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tags: |
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- translation |
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license: mit |
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--- |
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## About the model |
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It is a Turkish bert-based model created to determine the types of bullying that people use against each other in social media. |
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Included classes; |
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- Nötr |
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- Kızdırma/Hakaret |
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- Cinsiyetçilik |
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- Irkçılık |
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3388 tweets were used in the training of the model. Accordingly, the success rates in education are as follows; |
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| | Cinsiyetçilik | Irkçılık | Kızdırma | Nötr | |
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| ------ | ------ | ------ | ------ | ------ | |
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| Precision | 0.925 | 0.878 | 0.824 | 0.915 | |
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| Recall | 0.831 | 0.896 | 0.843 | 0.935 | |
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| F1 Score | 0.875 | 0.887 | 0.833 | 0.925 | |
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Accuracy : 0.886 |
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## Dependency |
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pip install torch torchvision torchaudio |
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pip install tf-keras |
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pip install transformers |
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pip install tensorflow |
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## Example |
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```sh |
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from transformers import AutoTokenizer, TextClassificationPipeline, TFBertForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("nanelimon/bert-base-turkish-bullying") |
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model = TFBertForSequenceClassification.from_pretrained("nanelimon/bert-base-turkish-bullying", from_pt=True) |
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pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer) |
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print(pipe('Bu bir denemedir hadi sende dene!')) |
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``` |
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Result; |
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```sh |
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[{'label': 'Nötr', 'score': 0.999175488948822}] |
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``` |
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- label= It shows which class the sent Turkish text belongs to according to the model. |
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- score= It shows the compliance rate of the Turkish text sent to the label found. |
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## Authors |
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- Seyma SARIGIL: seymasargil@gmail.com |
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- Elif SARIGIL KARA: elifsarigil@gmail.com |
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- Murat KOKLU: mkoklu@selcuk.edu.tr |
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- Alaaddin Erdinç DAL: aerdincdal@icloud.com |
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## License |
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gpl-3.0 |
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**Free Software, Hell Yeah!** |
