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
File size: 1,327 Bytes
e7a8573 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | {
"__version__": {
"pytorch": "2.12.0+cu130",
"sentence_transformers": "6.2.0",
"transformers": "5.22.0"
},
"default_prompt_name": null,
"model_type": "SentenceTransformer",
"prompts": {
"BitextMining": "task: search result | query: ",
"Classification": "task: classification | query: ",
"Clustering": "task: clustering | query: ",
"CodeRetrieval": "task: code retrieval | query: ",
"Document": "title: none | text: ",
"FactChecking": "task: fact checking | query: ",
"InstructionRetrieval": "task: code retrieval | query: ",
"MultilabelClassification": "task: classification | query: ",
"PairClassification": "task: sentence similarity | query: ",
"QuestionAnswering": "task: question answering | query: ",
"Reranking": "task: search result | query: ",
"Retrieval": "task: search result | query: ",
"Retrieval-document": "title: none | text: ",
"Retrieval-query": "task: search result | query: ",
"STS": "task: sentence similarity | query: ",
"SearchQuery": "task: search result | query: ",
"SentenceSimilarity": "task: sentence similarity | query: ",
"Summarization": "task: sentence similarity | query: ",
"document": "title: none | text: ",
"query": "task: search result | query: "
},
"similarity_fn_name": "cosine"
} |