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--- |
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license: mit |
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datasets: |
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- nanelimon/insult-dataset |
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language: |
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- tr |
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pipeline_tag: text-classification |
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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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| | INSULT | OTHER | PROFANITY | RACIST | SEXIST | |
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| ------ | ------ | ------ | ------ | ------ | ------ | |
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| Precision | 0.901 | 0.924 | 0.978 | 1.000 | 0.980 | |
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| Recall | 0.920 | 0.980 | 0.900 | 0.980 | 1.000 | |
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| F1 Score | 0.910 | 0.9514 | 0.937 | 0.989 | 0.990 | |
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- F-Score: 0.9559690799177005 |
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- Recall: 0.9559999999999998 |
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- Precision: 0.9570284225256961 |
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- Accuracy: 0.956 |
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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-offensive") |
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model = TFBertForSequenceClassification.from_pretrained("nanelimon/bert-base-turkish-offensive", from_pt=True) |
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pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True, top_k=2) |
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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': 'OTHER', 'score': 1.000}, {'label': 'INSULT', 'score': 0.000}]] |
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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: [email protected] |
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## License |
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gpl-3.0 |
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**Free Software, Hell Yeah!** |
