Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
JAX
ONNX
Safetensors
OpenVINO
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use sentence-transformers/msmarco-MiniLM-L6-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/msmarco-MiniLM-L6-v3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/msmarco-MiniLM-L6-v3") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use sentence-transformers/msmarco-MiniLM-L6-v3 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/msmarco-MiniLM-L6-v3") model = AutoModel.from_pretrained("sentence-transformers/msmarco-MiniLM-L6-v3") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 80970a9aa072cc6b447d85bfe19d9418a46d388f61a6183c2758f560d1956a9b
- Size of remote file:
- 90.9 MB
- SHA256:
- 5e3a29b2fc7bce0f6b0bdd35dcd6e6d1c1dd5fc191561d0b9c5d3aadf3891e0b
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