# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("H831A/yaqeen_out")
model = AutoModelForSequenceClassification.from_pretrained("H831A/yaqeen_out")Quick Links
yaqeen_out
This model is a fine-tuned version of CAMeL-Lab/bert-base-arabic-camelbert-mix on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0117
- Accuracy: 1.0
- F1 Fake: 1.0
- F1 Real: 1.0
- F1 Macro: 1.0
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Fake | F1 Real | F1 Macro |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 35 | 0.2376 | 0.9032 | 0.0 | 0.9492 | 0.4746 |
| 0.3653 | 2.0 | 70 | 0.0118 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0202 | 3.0 | 105 | 0.0009 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0202 | 4.0 | 140 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for H831A/yaqeen_out
Base model
CAMeL-Lab/bert-base-arabic-camelbert-mix
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="H831A/yaqeen_out")