Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use nzm97/Arabic_insults_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nzm97/Arabic_insults_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nzm97/Arabic_insults_detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nzm97/Arabic_insults_detection") model = AutoModelForSequenceClassification.from_pretrained("nzm97/Arabic_insults_detection") - Notebooks
- Google Colab
- Kaggle
Arabic_insults_detection
This model is a fine-tuned version of CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment on the None dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
Training results
Framework versions
- Transformers 4.46.2
- Pytorch 2.2.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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