Instructions to use moha/arabert_c19 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moha/arabert_c19 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="moha/arabert_c19")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("moha/arabert_c19") model = AutoModelForMaskedLM.from_pretrained("moha/arabert_c19") - Notebooks
- Google Colab
- Kaggle
| language: ar | |
| widget: | |
| - text: "لكي نتجنب فيروس [MASK]" | |
| # arabert_c19: An Arabert model pretrained on 1.5 million COVID-19 multi-dialect Arabic tweets | |
| **ARABERT COVID-19** [Arxiv URL](https://arxiv.org/pdf/2105.03143.pdf) is a pretrained (fine-tuned) version of the AraBERT v2 model (https://huggingface.co/aubmindlab/bert-base-arabertv02). The pretraining was done using 1.5 million multi-dialect Arabic tweets regarding the COVID-19 pandemic from the “Large Arabic Twitter Dataset on COVID-19” (https://arxiv.org/abs/2004.04315). | |
| The model can achieve better results for the tasks that deal with multi-dialect Arabic tweets in relation to the COVID-19 pandemic. | |
| # Classification results for multiple tasks including fake-news and hate speech detection when using arabert_c19 and mbert_ar_c19: | |
| For more details refer to the paper (link) | |
| | | arabert | mbert | distilbert multi | arabert Covid-19 | mbert Covid-19 | | |
| |------------------------------------|----------|----------|------------------|------------------|----------------| | |
| | Contains hate (Binary) | 0.8346 | 0.6675 | 0.7145 | `0.8649` | 0.8492 | | |
| | Talk about a cure (Binary) | 0.8193 | 0.7406 | 0.7127 | 0.9055 | `0.9176` | | |
| | News or opinion (Binary) | 0.8987 | 0.8332 | 0.8099 | `0.9163` | 0.9116 | | |
| | Contains fake information (Binary) | 0.6415 | 0.5428 | 0.4743 | `0.7739` | 0.7228 | | |
| # Preprocessing | |
| ```python | |
| from arabert.preprocess import ArabertPreprocessor | |
| model_name="moha/arabert_c19" | |
| arabert_prep = ArabertPreprocessor(model_name=model_name) | |
| text = "للوقايه من عدم انتشار كورونا عليك اولا غسل اليدين بالماء والصابون وتكون عملية الغسل دقيقه تشمل راحة اليد الأصابع التركيز على الإبهام" | |
| arabert_prep.preprocess(text) | |
| ``` | |
| # Citation | |
| Please cite as: | |
| ``` bibtex | |
| @misc{ameur2021aracovid19mfh, | |
| title={AraCOVID19-MFH: Arabic COVID-19 Multi-label Fake News and Hate Speech Detection Dataset}, | |
| author={Mohamed Seghir Hadj Ameur and Hassina Aliane}, | |
| year={2021}, | |
| eprint={2105.03143}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CL} | |
| } | |
| ``` | |
| # Contacts | |
| **Hadj Ameur**: [Github](https://github.com/MohamedHadjAmeur) | <[email protected]> | <[email protected]> | |