Instructions to use FaisaI/tadabur-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FaisaI/tadabur-embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="FaisaI/tadabur-embedding", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("FaisaI/tadabur-embedding", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 20d119e61376435a64657cfe5764087f36f0c50f1e34ffc1225adaf5ee38a86a
- Size of remote file:
- 1.07 GB
- SHA256:
- e9615dcb2721a63ca1a429760eb6352efeb950b4e5d3705486152eeb2034b787
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