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
Update README.md
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README.md
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A BERT-based model fine-tuned to estimate the Arabic Level od Dialectness of text.
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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A BERT-based model fine-tuned to estimate the Arabic Level od Dialectness of text.
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Model | Link on 🤗
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**Sentence-ALDi** (random seed: 42) | https://huggingface.co/AMR-KELEG/Sentence-ALDi
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Sentence-ALDi (random seed: 30) | https://huggingface.co/AMR-KELEG/Sentence-ALDi-30
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Sentence-ALDi (random seed: 50) | https://huggingface.co/AMR-KELEG/Sentence-ALDi-50
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**Token-DI** (random seed: 42) | https://huggingface.co/AMR-KELEG/ALDi-Token-DI
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Token-DI (random seed: 30) | https://huggingface.co/AMR-KELEG/ALDi-Token-DI-30
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Token-DI (random seed: 50) | https://huggingface.co/AMR-KELEG/ALDi-Token-DI-50
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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