Instructions to use metga97/Modern-EgyBert-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use metga97/Modern-EgyBert-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="metga97/Modern-EgyBert-Base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("metga97/Modern-EgyBert-Base") model = AutoModelForMaskedLM.from_pretrained("metga97/Modern-EgyBert-Base") - Notebooks
- Google Colab
- Kaggle
egyptian_modernbert_fineweb2
Modern EgyBert is a ๐ค Modern Bert transformers model trained on the Egyptian Arabic Split of the FineWeb2 dataset.
Language(s) (NLP): Egyptian Arabic
Finetuned from model: Modern Bert Base
It achieves the following results on the evaluation set:
- eval_loss: 2.2641
- eval_runtime: 134.9987
- eval_samples_per_second: 68.267
- eval_steps_per_second: 8.533
- epoch: 4
- step: 190000
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.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
- num_epochs: 4
Framework versions
- Transformers 4.49.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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