Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use sentence-transformers/all-MiniLM-L12-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/all-MiniLM-L12-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/all-MiniLM-L12-v1") 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/all-MiniLM-L12-v1 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L12-v1", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a488351a551bcffae1f58764433a9f0cdd2c72080aac9e7bbbeac7e9fe9a4cfe
- Size of remote file:
- 134 MB
- SHA256:
- 5be981f4605f0c2918efd0db60e8273ac0de60cb8d39eb1a874a5b7a7ceb36aa
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