Instructions to use Sifal/dzarabert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sifal/dzarabert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Sifal/dzarabert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Sifal/dzarabert") model = AutoModelForMaskedLM.from_pretrained("Sifal/dzarabert") - Notebooks
- Google Colab
- Kaggle
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<img src="https://raw.githubusercontent.com/alger-ia/dziribert/main/dziribert_drawing.png" alt="drawing" width="25%" height="25%" align="right"/>
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DzarbiBert is a pruned model of first Transformer-based Language Model that has been pre-trained specifically for the Algerian Dialect ([DziriBERT](https://huggingface.co/alger-ia/dziribert)). This pruned version handles Algerian text contents written using Arabic letters. It sets new state of the art results on Algerian text classification datasets, even if it has been pre-trained on much less data (~1 million tweets).
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<img src="https://raw.githubusercontent.com/alger-ia/dziribert/main/dziribert_drawing.png" alt="drawing" width="25%" height="25%" align="right"/>
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# Dzarabert
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DzarbiBert is a pruned model of first Transformer-based Language Model that has been pre-trained specifically for the Algerian Dialect ([DziriBERT](https://huggingface.co/alger-ia/dziribert)). This pruned version handles Algerian text contents written using Arabic letters. It sets new state of the art results on Algerian text classification datasets, even if it has been pre-trained on much less data (~1 million tweets).
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