Instructions to use huggingface-mic/all-MiniLM-L6-v2-mic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use huggingface-mic/all-MiniLM-L6-v2-mic with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("huggingface-mic/all-MiniLM-L6-v2-mic") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
all-MiniLM-L6-v2-mic
Slim model weights, executable usage and framework expectations in one repository.
- Contract manifest
- Sentence Transformers usage
- Sentence Transformers expectations
- Fixture provenance
- How the fixture was shrunk
Contract
SentenceTransformer: sentence-transformers/all-MiniLM-L6-v2
Runnable usage and the framework expectations directory are linked above.
Slim model
SentenceTransformer MIC fixture
Shrunk pipeline for sentence-transformers/all-MiniLM-L6-v2. Its weights are random and do not represent the source model's retrieval quality.
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Model tree for huggingface-mic/all-MiniLM-L6-v2-mic
Base model
nreimers/MiniLM-L6-H384-uncased Quantized
sentence-transformers/all-MiniLM-L6-v2