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:
- 90371b4907beb801cb33035a44b1a42987525b7c017a724d49becc14958b603f
- Size of remote file:
- 471 MB
- SHA256:
- 16cc9e54df6e083272378abec2d75dc34d7a48b5276db3ccc050d18de672ac59
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