Seymasa's picture
Update README.md
ab8dbfb verified
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

License

gpl-3.0

Free Software, Hell Yeah!