Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
Transformers
distilbert
feature-extraction
text-embeddings-inference
Instructions to use sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking") model = AutoModel.from_pretrained("sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 12707a6efcedb36b356f08b61a2669e50e47a0e04be66ed5f91dbb3246da9d64
- Size of remote file:
- 135 MB
- SHA256:
- e431f7c80e3fa162c22274621f2f27c1b9790e0d7c3b397ba9f3e22af40c41fe
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