Sentence Similarity
sentence-transformers
PyTorch
Rust
ONNX
Safetensors
OpenVINO
Transformers
English
bert
feature-extraction
Eval Results
text-embeddings-inference
Instructions to use sentence-transformers/all-MiniLM-L12-v2 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-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/all-MiniLM-L12-v2") 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-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L12-v2") model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L12-v2") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f17b5ff5558f6430b22d766a5600f4794a6462936aa882599c777fb78f2eef77
- Size of remote file:
- 134 MB
- SHA256:
- 54609dea3ff88f3167f049eeadbfe780b1173a3117bfac862134ebcd8ce33661
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