Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("eltaiefaymen/sada-eou-balanced")
model = AutoModelForSequenceClassification.from_pretrained("eltaiefaymen/sada-eou-balanced")Quick Links
sada-eou-balanced
This model is a fine-tuned version of aubmindlab/bert-base-arabertv02 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1476
- Accuracy: 0.9636
- F1: 0.9635
- F1 Macro: 0.9635
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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: 300
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.57.2
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
- Downloads last month
- 2
Model tree for eltaiefaymen/sada-eou-balanced
Base model
aubmindlab/bert-base-arabertv02
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eltaiefaymen/sada-eou-balanced")