metadata
license: mit
datasets:
- nanelimon/insult-dataset
language:
- tr
pipeline_tag: text-classification
About the model
This model is designed for text classification, specifically for identifying offensive content in Turkish text. The model classifies text into five categories: INSULT, OTHER, PROFANITY, RACIST, and SEXIST.
Model Metrics
| 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
Training Information
- Device : macOS 14.5 23F79 arm64 | GPU: Apple M2 Max | Memory: 5840MiB / 32768MiB
- Training completed in 0:22:54 (hh:mm:ss)
- Optimizer: AdamW
- learning_rate: 2e-5
- eps: 1e-8
- epochs: 10
- Batch size: 64
Dependency
pip install torch torchvision torchaudio
pip install tf-keras
pip install transformers
pip install tensorflow
Example
from transformers import AutoTokenizer, TFAutoModelForSequenceClassification, TextClassificationPipeline
# Load the tokenizer and model
model_name = "nanelimon/bert-base-turkish-offensive"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
# Create the pipeline
pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True, top_k=2)
# Test the pipeline
print(pipe('Bu bir denemedir hadi sende dene!'))
Result;
[[{'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!