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--- |
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language: ar |
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datasets: |
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- oscar |
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- wikipedia |
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tags: |
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- ar |
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- masked-lm |
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--- |
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# Arabic-ALBERT Base |
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Arabic edition of ALBERT Base pretrained language model |
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_If you use any of these models in your work, please cite this work as:_ |
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``` |
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@software{ali_safaya_2020_4718724, |
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author = {Ali Safaya}, |
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title = {Arabic-ALBERT}, |
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month = aug, |
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year = 2020, |
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publisher = {Zenodo}, |
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version = {1.0.0}, |
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doi = {10.5281/zenodo.4718724}, |
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url = {https://doi.org/10.5281/zenodo.4718724} |
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} |
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``` |
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## Pretraining data |
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The models were pretrained on ~4.4 Billion words: |
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- Arabic version of [OSCAR](https://oscar-corpus.com/) (unshuffled version of the corpus) - filtered from [Common Crawl](http://commoncrawl.org/) |
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- Recent dump of Arabic [Wikipedia](https://dumps.wikimedia.org/backup-index.html) |
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__Notes on training data:__ |
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- Our final version of corpus contains some non-Arabic words inlines, which we did not remove from sentences since that would affect some tasks like NER. |
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- Although non-Arabic characters were lowered as a preprocessing step, since Arabic characters do not have upper or lower case, there is no cased and uncased version of the model. |
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- The corpus and vocabulary set are not restricted to Modern Standard Arabic, they contain some dialectical Arabic too. |
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## Pretraining details |
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- These models were trained using Google ALBERT's github [repository](https://github.com/google-research/albert) on a single TPU v3-8 provided for free from [TFRC](https://www.tensorflow.org/tfrc). |
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- Our pretraining procedure follows training settings of bert with some changes: trained for 7M training steps with batchsize of 64, instead of 125K with batchsize of 4096. |
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## Models |
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| | albert-base | albert-large | albert-xlarge | |
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|:---:|:---:|:---:|:---:| |
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| Hidden Layers | 12 | 24 | 24 | |
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| Attention heads | 12 | 16 | 32 | |
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| Hidden size | 768 | 1024 | 2048 | |
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## Results |
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For further details on the models performance or any other queries, please refer to [Arabic-ALBERT](https://github.com/KUIS-AI-Lab/Arabic-ALBERT/) |
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## How to use |
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You can use these models by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this: |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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# loading the tokenizer |
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base_tokenizer = AutoTokenizer.from_pretrained("kuisailab/albert-base-arabic") |
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# loading the model |
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base_model = AutoModelForMaskedLM.from_pretrained("kuisailab/albert-base-arabic") |
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``` |
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## Acknowledgement |
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Thanks to Google for providing free TPU for the training process and for Huggingface for hosting these models on their servers 😊 |
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