Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use emma2026study/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 emma2026study/paraphrase-multilingual-MiniLM-L12-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("emma2026study/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 emma2026study/paraphrase-multilingual-MiniLM-L12-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("emma2026study/paraphrase-multilingual-MiniLM-L12-v2") model = AutoModel.from_pretrained("emma2026study/paraphrase-multilingual-MiniLM-L12-v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 46216267de82d81a2aa70270ad04ad82028d0958b971760c1caa87e7793dc937
- Size of remote file:
- 471 MB
- SHA256:
- 22150b6ba00e477c7f816f1988d028fff924e2b52e14540889690c72c5add40e
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