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 word_matching.json from g3ida/ArabicWordsEmbedding: direct link, hf CLI and curl.
- Browser
- Download file 238 Bytes
-
https://huggingface.co/g3ida/ArabicWordsEmbedding/resolve/main/word_matching.json
- Command line
-
hf download hf://g3ida/ArabicWordsEmbedding/word_matching.json
-
curl -L -o word_matching.json https://huggingface.co/g3ida/ArabicWordsEmbedding/resolve/main/word_matching.json
238 Bytes
| { | |
| "image_height": 96, | |
| "max_soft_tokens": 70, | |
| "text_prompt": null, | |
| "text_normalisation": "arabic.core_word (strip tashkeel + edge punctuation)", | |
| "note": "use scripts/match_page.py (embed.patchify(height=96)) for image preprocessing" | |
| } |