File size: 1,018 Bytes
ea338bf fe74bfa 451b1d6 44c2bb9 451b1d6 44c2bb9 451b1d6 ea338bf 451b1d6 ea338bf 451b1d6 ea338bf 451b1d6 ea338bf 451b1d6 ea338bf 451b1d6 ea338bf 451b1d6 ea338bf 451b1d6 ea338bf 451b1d6 ea338bf 44c2bb9 451b1d6 ea338bf 44c2bb9 ea338bf 451b1d6 ea338bf 451b1d6 ea338bf 44c2bb9 ea338bf 44c2bb9 ea338bf 451b1d6 ea338bf 44c2bb9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 |
---
library_name: transformers
license: mit
language:
- tr
metrics:
- accuracy
- f1
- precision
- recall
pipeline_tag: text-classification
---
# About the Model
This model is a Turkish-based convBERT model created to classify Turkish social media bullying data. Classes included in the model
* Cinsiyetçilik
* Irkçılık
* Kızdırma
* Nötr
The model was trained from a dataset containing 2981 Twitter data.
## Dependency
pip install transformers
pip install torch
## Example
```python
from transformers import ConvBertTokenizer, ConvBertForSequenceClassification,TextClassificationPipeline
tokenizer = ConvBertTokenizer.from_pretrained("AIZinu/convBERT-turk-based-cyberbullying")
model = ConvBertForSequenceClassification.from_pretrained("AIZinu/convBERT-turk-based-cyberbullying")
pipe= TextClassificationPipeline(model=model, tokenizer=tokenizer)
print(pipe("Hadi modeli deneyelim"))
```
Result:
[{'label': 'LABEL_3', 'score': 0.9994651675224304}]
## Model Card Authors
* Bilge Nur BEKAR |