Feature Extraction
Transformers
Safetensors
Arabic
embedding_gemma2
arabic
ocr
document-ai
word-matching
multimodal-embedding
embeddinggemma
Instructions to use g3ida/ArabicWordsEmbedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use g3ida/ArabicWordsEmbedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="g3ida/ArabicWordsEmbedding")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("g3ida/ArabicWordsEmbedding") model = AutoModel.from_pretrained("g3ida/ArabicWordsEmbedding", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from g3ida/ArabicWordsEmbedding: direct link, hf CLI and curl.
- Browser
- Download file 32.2 MB
-
https://huggingface.co/g3ida/ArabicWordsEmbedding/resolve/main/tokenizer.json
- Command line
-
hf download hf://g3ida/ArabicWordsEmbedding/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/g3ida/ArabicWordsEmbedding/resolve/main/tokenizer.json
32.2 MB
- Xet hash:
- ff29dc00bacbe2db29a89eee9cd86b61be7936ed3d1a755ff69d1b259a18a7c7
- Size of remote file:
- 32.2 MB
- SHA256:
- 4d777ef5bdc1aa36227abdfb77c3e49e7b9c892d16e1b6bda41c393504828be4
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