Instructions to use akhooli/ModernBERT-ar-base-tiny_0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akhooli/ModernBERT-ar-base-tiny_0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="akhooli/ModernBERT-ar-base-tiny_0")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("akhooli/ModernBERT-ar-base-tiny_0") model = AutoModelForMaskedLM.from_pretrained("akhooli/ModernBERT-ar-base-tiny_0") - Notebooks
- Google Colab
- Kaggle
ModernBERT-ar-base-small3
This model is a fine-tuned version of on an unknown 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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
- lr_scheduler_warmup_steps: 500
- training_steps: 80000
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.49.0.dev0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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