Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use oxygeneDev/paraphrase-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use oxygeneDev/paraphrase-multilingual with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("oxygeneDev/paraphrase-multilingual") 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 oxygeneDev/paraphrase-multilingual with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("oxygeneDev/paraphrase-multilingual") model = AutoModel.from_pretrained("oxygeneDev/paraphrase-multilingual", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4b26835fac72692da8ad97779b0a28bae3ce1891b435b0887509040a6b498654
- Size of remote file:
- 470 MB
- SHA256:
- 338ef03c2838d5a659d36e1ce5b7a1dc2d2a66a430a9e6f499de6dc39f663850
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