#!/usr/bin/env python3 """ ZamAI Multilingual Embeddings - Hugging Face Hub Deployment Script This script pushes the ZamAI multilingual embeddings model to Hugging Face Hub """ import os import subprocess import sys from pathlib import Path from huggingface_hub import HfApi, login, create_repo def check_huggingface_auth(): """Check if user is authenticated with Hugging Face""" try: api = HfApi() user_info = api.whoami() print(f"✅ Authenticated as: {user_info['name']}") return True except Exception as e: print("❌ Not authenticated with Hugging Face") print("Please run: huggingface-cli login") return False def create_model_card(): """Create a comprehensive model card for the ZamAI model""" model_card_content = """--- license: apache-2.0 datasets: - tasal9/Pashto_Dataset language: - ps - en - ar - ur - fa library_name: sentence-transformers tags: - multilingual - embeddings - semantic-search - pashto - chromadb - llamaindex - cross-lingual - afghanistan - zamai pipeline_tag: feature-extraction model-index: - name: Multilingual-ZamAI-Embeddings results: [] widget: - source_sentence: "This is a sample sentence in English." sentences: - "This sentence is similar to the first one." - "دا جمله د لومړۍ جملې سره ورته ده." - "This sentence has nothing to do with the others." example_title: "English to multilingual similarity" - source_sentence: "دا په پښتو کې یوه نمونه جمله ده." sentences: - "This is a sample sentence in English." - "دا جمله د لومړۍ جملې سره ورته ده." - "زه د پښتو ژبې زده کړه کوم." example_title: "Pashto to multilingual similarity" --- # ZamAI Multilingual Embeddings This model provides state-of-the-art multilingual sentence embeddings with a special focus on Pashto language support. Built on the foundation of `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`, this model enables semantic search, document retrieval, and cross-lingual understanding across 50+ languages. ## Model Details - **Model Type**: Sentence Transformer (BERT-based) - **Base Model**: [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) - **Languages Supported**: 50+ including Pashto (ps), English (en), Arabic (ar), Urdu (ur), Farsi (fa), and more - **Max Sequence Length**: 512 tokens - **Output Dimensionality**: 384 - **License**: Apache 2.0 ## Key Features - **Cross-lingual Understanding**: Retrieve semantically similar content across different languages - **Pashto Language Support**: Optimized for Pashto language processing and understanding - **Vector Database Integration**: Ready-to-use with ChromaDB and LlamaIndex - **High Performance**: Efficient processing suitable for real-time applications ## Usage ### Basic Usage with Sentence Transformers ```python from sentence_transformers import SentenceTransformer import numpy as np # Load the model model = SentenceTransformer('tasal9/Multilingual-ZamAI-Embeddings') # English sentences sentences_en = [ "This is a sample sentence in English.", "This sentence is similar to the first one." ] # Pashto sentences sentences_ps = [ "دا په پښتو کې یوه نمونه جمله ده.", "دا جمله د لومړۍ جملې سره ورته ده." ] # Get embeddings embeddings_en = model.encode(sentences_en) embeddings_ps = model.encode(sentences_ps) # Calculate cross-lingual similarity from numpy import dot from numpy.linalg import norm def cosine_similarity(a, b): return dot(a, b) / (norm(a) * norm(b)) # Compare English and Pashto sentences similarity = cosine_similarity(embeddings_en[0], embeddings_ps[0]) print(f"Cross-lingual similarity: {similarity:.4f}") ``` ### Advanced Usage with ChromaDB and LlamaIndex ```python from llama_index.embeddings.huggingface import HuggingFaceEmbedding from llama_index.vector_stores.chroma import ChromaVectorStore from llama_index.core import StorageContext, VectorStoreIndex import chromadb # Initialize the embedding model embed_model = HuggingFaceEmbedding(model_name="tasal9/Multilingual-ZamAI-Embeddings") # Set up ChromaDB chroma_client = chromadb.PersistentClient(path="./chroma_db") collection = chroma_client.get_or_create_collection("multilingual_collection") vector_store = ChromaVectorStore(chroma_collection=collection) storage_context = StorageContext.from_defaults(vector_store=vector_store) # Create index with your documents # index = VectorStoreIndex.from_documents(documents, storage_context=storage_context, embed_model=embed_model) # query_engine = index.as_query_engine() # Query in any language # result = query_engine.query("What is the capital of Afghanistan?") # result_ps = query_engine.query("د افغانستان پلازمېنه څه ده؟") ``` ## Performance The model demonstrates excellent cross-lingual performance: - **English-English**: High semantic similarity detection - **Pashto-Pashto**: Native language understanding and similarity - **Cross-lingual (English-Pashto)**: Strong cross-lingual semantic alignment - **Multilingual**: Supports 50+ languages with consistent performance ## Applications - **Semantic Search**: Find relevant documents across multiple languages - **Cross-lingual Information Retrieval**: Retrieve Pashto content using English queries and vice versa - **Document Similarity**: Compare documents in different languages - **Question Answering**: Build multilingual QA systems - **Content Recommendation**: Recommend similar content across languages ## Technical Details - **Architecture**: BERT-based transformer model - **Training Data**: Multilingual parallel and monolingual corpora - **Optimization**: Optimized for semantic similarity tasks - **Integration**: Compatible with Hugging Face Transformers, Sentence Transformers, LlamaIndex, and ChromaDB ## Citation If you use this model in your research, please cite: ```bibtex @misc{zamai-multilingual-embeddings-2024, title={ZamAI Multilingual Embeddings: Cross-lingual Sentence Transformers with Pashto Support}, author={ZamAI Team}, year={2024}, url={https://huggingface.co/tasal9/Multilingual-ZamAI-Embeddings} } ``` ## License This model is released under the Apache 2.0 License. See the [LICENSE](LICENSE) file for details. ## Contact For questions or support, please open an issue on the [model repository](https://huggingface.co/tasal9/Multilingual-ZamAI-Embeddings) or contact the ZamAI team. """ with open("README.md", "w", encoding="utf-8") as f: f.write(model_card_content) print("✅ Model card created: README.md") def prepare_repository(): """Prepare the repository for upload""" print("🔧 Preparing repository...") # Create model card create_model_card() # Create additional files files_to_create = { ".gitattributes": """*.7z filter=lfs diff=lfs merge=lfs -text *.arrow filter=lfs diff=lfs merge=lfs -text *.bin filter=lfs diff=lfs merge=lfs -text *.bz2 filter=lfs diff=lfs merge=lfs -text *.ckpt filter=lfs diff=lfs merge=lfs -text *.ftz filter=lfs diff=lfs merge=lfs -text *.gz filter=lfs diff=lfs merge=lfs -text *.h5 filter=lfs diff=lfs merge=lfs -text *.joblib filter=lfs diff=lfs merge=lfs -text *.lfs.* filter=lfs diff=lfs merge=lfs -text *.mlmodel filter=lfs diff=lfs merge=lfs -text *.model filter=lfs diff=lfs merge=lfs -text *.msgpack filter=lfs diff=lfs merge=lfs -text *.npy filter=lfs diff=lfs merge=lfs -text *.npz filter=lfs diff=lfs merge=lfs -text *.onnx filter=lfs diff=lfs merge=lfs -text *.ot filter=lfs diff=lfs merge=lfs -text *.parquet filter=lfs diff=lfs merge=lfs -text *.pb filter=lfs diff=lfs merge=lfs -text *.pickle filter=lfs diff=lfs merge=lfs -text *.pkl filter=lfs diff=lfs merge=lfs -text *.pt filter=lfs diff=lfs merge=lfs -text *.pth filter=lfs diff=lfs merge=lfs -text *.rar filter=lfs diff=lfs merge=lfs -text *.safetensors filter=lfs diff=lfs merge=lfs -text saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.tar.* filter=lfs diff=lfs merge=lfs -text *.tar filter=lfs diff=lfs merge=lfs -text *.tflite filter=lfs diff=lfs merge=lfs -text *.tgz filter=lfs diff=lfs merge=lfs -text *.wasm filter=lfs diff=lfs merge=lfs -text *.xz filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text """, "LICENSE": """Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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END OF TERMS AND CONDITIONS""" } for filename, content in files_to_create.items(): with open(filename, "w", encoding="utf-8") as f: f.write(content) print(f"✅ Created: {filename}") def push_to_huggingface(repo_name="tasal9/Multilingual-ZamAI-Embeddings"): """Push the model to Hugging Face Hub""" # Check authentication if not check_huggingface_auth(): print("\n🔑 Please authenticate with Hugging Face first:") print("huggingface-cli login") return False try: # Prepare repository prepare_repository() # Create repository on Hugging Face Hub api = HfApi() try: print(f"🚀 Creating repository: {repo_name}") create_repo( repo_id=repo_name, repo_type="model", exist_ok=True, private=False ) print(f"✅ Repository created/verified: {repo_name}") except Exception as e: print(f"ℹ️ Repository may already exist: {e}") # Upload files print("📤 Uploading files to Hugging Face Hub...") # Upload all files in the current directory current_dir = Path(".") files_to_upload = [ "README.md", "requirements.txt", "setup.py", "demo.py", "simple_demo.py", "indexer.py", "modeling.py", "config.json", "LICENSE", ".gitattributes" ] for file_path in files_to_upload: if os.path.exists(file_path): try: api.upload_file( path_or_fileobj=file_path, path_in_repo=file_path, repo_id=repo_name, commit_message=f"Add {file_path}" ) print(f"✅ Uploaded: {file_path}") except Exception as e: print(f"⚠️ Warning uploading {file_path}: {e}") print(f"\n🎉 Successfully pushed ZamAI Multilingual Embeddings to Hugging Face Hub!") print(f"🔗 Model URL: https://huggingface.co/{repo_name}") print(f"📖 Usage: model = SentenceTransformer('{repo_name}')") return True except Exception as e: print(f"❌ Error pushing to Hugging Face: {e}") return False if __name__ == "__main__": print("🚀 ZamAI Multilingual Embeddings - Hugging Face Deployment") print("=" * 60) # Check if we're in the right directory if not os.path.exists("setup.py"): print("❌ Please run this script from the Multilingual-ZamAI-Embeddings directory") sys.exit(1) # Push to Hugging Face success = push_to_huggingface() if success: print("\n✨ Deployment completed successfully!") print("Your model is now available on Hugging Face Hub") else: print("\n❌ Deployment failed. Please check the errors above.") sys.exit(1)