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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- rag |
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base_model: sentence-transformers/all-MiniLM-L6-v2 |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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license: apache-2.0 |
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language: |
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- en |
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--- |
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# The Fastest Text Embedding Model: tabularisai/all-MiniLM-L2-v2 |
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This model is distilled from [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2), delivering **almost 2 times faster inference** in comparasion to the smallest all-MiniLM-L6-v2 model, while maintaining strong accuracy on CPU and GPU. |
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## Usage |
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### Retrieval-Augmented Generation (RAG) Example |
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Use this model as a retriever in a RAG pipeline: |
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```python |
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from sentence_transformers import SentenceTransformer, util |
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import faiss |
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import numpy as np |
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# Load embedding model |
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model = SentenceTransformer("tabularisai/all-MiniLM-L2-v2") |
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# Your 5 simple documents |
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documents = [ |
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"Renewable energy comes from natural sources.", |
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"Solar panels convert sunlight into electricity.", |
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"Wind turbines harness wind power.", |
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"Fossil fuels are non-renewable sources of energy.", |
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"Hydropower uses water to generate electricity." |
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] |
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# Embed documents |
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doc_embeddings = model.encode(documents, convert_to_numpy=True) |
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# Create FAISS index |
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dim = doc_embeddings.shape[1] |
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index = faiss.IndexFlatL2(dim) |
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index.add(doc_embeddings) |
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# Query |
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query = "What are the benefits of renewable energy?" |
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query_embedding = model.encode([query], convert_to_numpy=True) |
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# Search top 3 similar docs |
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D, I = index.search(query_embedding, k=3) |
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# Print results |
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print("Query:", query) |
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print("\nTop 3 similar documents:") |
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for rank, idx in enumerate(I[0]): |
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print(f"{rank+1}. {documents[idx]} (score: {D[0][rank]:.4f})") |
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``` |
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### Sentence Embedding Example |
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Install the library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Load the model and encode sentences: |
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```python |
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from sentence_transformers import SentenceTransformer |
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model = SentenceTransformer("tabularisai/all-MiniLM-L2-v2") |
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sentences = [ |
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"The weather is lovely today.", |
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"It's so sunny outside!", |
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"He drove to the stadium.", |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) # [3, 384] |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) # [3, 3] |
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``` |
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<table align="center"> |
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<tr> |
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<td align="center"> |
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<a href="https://www.linkedin.com/company/tabularis-ai/"> |
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<img src="https://cdn.jsdelivr.net/gh/simple-icons/simple-icons/icons/linkedin.svg" alt="LinkedIn" width="30" height="30"> |
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</a> |
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</td> |
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<td align="center"> |
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<a href="https://x.com/tabularis_ai"> |
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<img src="https://cdn.jsdelivr.net/gh/simple-icons/simple-icons/icons/x.svg" alt="X" width="30" height="30"> |
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</a> |
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</td> |
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<td align="center"> |
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<a href="https://github.com/tabularis-ai"> |
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<img src="https://cdn.jsdelivr.net/gh/simple-icons/simple-icons/icons/github.svg" alt="GitHub" width="30" height="30"> |
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</a> |
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</td> |
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<td align="center"> |
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<a href="https://tabularis.ai"> |
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<img src="https://cdn.jsdelivr.net/gh/simple-icons/simple-icons/icons/internetarchive.svg" alt="Website" width="30" height="30"> |
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</a> |
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</td> |
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</tr> |
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</table> |
