Fill-Mask
Transformers
PyTorch
xlm-roberta
Dialectal Arabic
Arabic
sequence labeling
Named entity recognition
Part-of-speech tagging
Zero-shot transfer learning
bert
Instructions to use 3ebdola/Dialectal-Arabic-XLM-R-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 3ebdola/Dialectal-Arabic-XLM-R-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="3ebdola/Dialectal-Arabic-XLM-R-Base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("3ebdola/Dialectal-Arabic-XLM-R-Base") model = AutoModelForMaskedLM.from_pretrained("3ebdola/Dialectal-Arabic-XLM-R-Base") - Notebooks
- Google Colab
- Kaggle
Abdellah EL MEKKI commited on
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README.md
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### About the Dialectal-Arabic-XLM-R-Base model
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We have built a 5 million Tweets corpus from Twitter. The crawled tweets cover the dialects of the four Arabic world regions (EGY, GLF, LEV, and MAG regions), as well as MSA. The collected corpus consists of one million (1M) tweets per Arabic variant. We did not perform any text pre-processing on the tweets, except by removing tweets that have a small length (tweets containing less than four words).
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### Usage
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### About the Dialectal-Arabic-XLM-R-Base model
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This model is an trained as a further pre-trained of XLM-RoBERTa base using the Masked-language modeling on a dialectal Arabic corpus.
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### About the Dialectal-Arabic-XLM-R-Base model training corpora
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We have built a 5 million Tweets corpus from Twitter. The crawled tweets cover the dialects of the four Arabic world regions (EGY, GLF, LEV, and MAG regions), as well as MSA. The collected corpus consists of one million (1M) tweets per Arabic variant. We did not perform any text pre-processing on the tweets, except by removing tweets that have a small length (tweets containing less than four words).
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### Usage
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