Instructions to use AMR-KELEG/Sentence-ALDi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AMR-KELEG/Sentence-ALDi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AMR-KELEG/Sentence-ALDi")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AMR-KELEG/Sentence-ALDi") model = AutoModelForSequenceClassification.from_pretrained("AMR-KELEG/Sentence-ALDi") - Notebooks
- Google Colab
- Kaggle
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- **Model type:** Regression head on top of a BERT-based model fine-tuned for estimating the Arabic Level of Dialectness of text.
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- **Language(s) (NLP):** Arabic.
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<!--- **License:** [More Information Needed] -->
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- **Finetuned from model
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### Citation
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- **Model type:** Regression head on top of a BERT-based model fine-tuned for estimating the Arabic Level of Dialectness of text.
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- **Language(s) (NLP):** Arabic.
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<!--- **License:** [More Information Needed] -->
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- **Finetuned from model:** [MarBERT](https://huggingface.co/UBC-NLP/MARBERT)
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- **Dataset**: [AOC-ALDi](https://github.com/AMR-KELEG/ALDi/blob/master/data/AOC-ALDi.tar.gz)
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### Citation
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