Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use sentence-transformers/paraphrase-multilingual-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/paraphrase-multilingual-MiniLM-L12-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2") 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] - Transformers
How to use sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2") model = AutoModel.from_pretrained("sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4042e40873843d80568f133b625a04b27e90a6536b9abc2df10225fcd454a851
- Size of remote file:
- 9.08 MB
- SHA256:
- 2c3387be76557bd40970cec13153b3bbf80407865484b209e655e5e4729076b8
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