Instructions to use Nada81/Nada_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nada81/Nada_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Nada81/Nada_Model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Nada81/Nada_Model") model = AutoModel.from_pretrained("Nada81/Nada_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Nada81/Nada_Model: direct link, hf CLI and curl.
- Browser
- Download file 512 Bytes
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https://huggingface.co/Nada81/Nada_Model/resolve/main/README.md
- Command line
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hf download hf://Nada81/Nada_Model/README.md
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curl -L -o README.md https://huggingface.co/Nada81/Nada_Model/resolve/main/README.md
512 Bytes
metadata
language:
- ar
- en
- multilingual
license: mit
Model Title
Your model description and additional information go here.
Nada_Model
Multilingual sentiment classification (Label_0: mixed, Label_1: negative, Label_2: positive) of Arabic reviews by fine-tuning XLM-Roberta-Large. Zero shot classification of other languages (also works in mixed languages - ex. Arabic & English). Mixed category is not accurate and may confuse other classes (was based on a rate of 3 out of 5 in reviews).