Instructions to use CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment") model = AutoModelForSequenceClassification.from_pretrained("CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment") - Inference
- Notebooks
- Google Colab
- Kaggle
Multilingual model — testing for mobile deployment
#6
by 3morixd - opened
This model covers Finnish, German, Telugu, Hebrew, Arabic — exactly what we need for global mobile AI.
At Dispatch AI (FZE, UAE), we're building mobile AI that works for everyone. We benchmark multilingual models on our 40-phone farm (Snapdragon 865) to check quality retention across languages after 4-bit quantization.
Would love to see multilingual eval at different quantization levels.
- Dispatch AI (FZE), Sharjah UAE