Sentence Similarity
sentence-transformers
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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
Remove deprecated (SEB) evaluation results section
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README.md
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## Evaluation Results
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For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
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## Full Model Architecture
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```
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SentenceTransformer(
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## Full Model Architecture
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```
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SentenceTransformer(
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