Instructions to use MutazYoune/Ara_DialectBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MutazYoune/Ara_DialectBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="MutazYoune/Ara_DialectBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("MutazYoune/Ara_DialectBERT") model = AutoModelForMaskedLM.from_pretrained("MutazYoune/Ara_DialectBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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datasets:
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- HARD-Arabic-Dataset
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We used a pretrained model to further train it on [HARD-Arabic-Dataset](https://github.com/elnagara/HARD-Arabic-Dataset), the weights were initialized using [CAMeL-Lab](https://huggingface.co/CAMeL-Lab/bert-base-camelbert-msa-eighth) "bert-base-camelbert-msa-eighth" model
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datasets:
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# Ara-dialect-BERT
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We used a pretrained model to further train it on [HARD-Arabic-Dataset](https://github.com/elnagara/HARD-Arabic-Dataset), the weights were initialized using [CAMeL-Lab](https://huggingface.co/CAMeL-Lab/bert-base-camelbert-msa-eighth) "bert-base-camelbert-msa-eighth" model
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