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:
# pip install -U transformers accelerate # 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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Request: DOI
#40
by tuandebu - opened
i need to do to analysis my project dataa
Hello!
I normally don't make DOIs for these models, but you can cite the paper (https://huggingface.co/papers/1908.10084) or link to the model instead, if you have to reference it in your writings. Beyond that, the Apache 2.0 license allows you to do a lot of things, so you're free to use it however you want.
- Tom Aarsen
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