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:
- 4047f61214150fcbff21d9dccdfdf5c63166996c80428c373b62aab29df24998
- Size of remote file:
- 539 MB
- SHA256:
- f83787ab90e0b4939f0fe031d95d37cf8cec63c12e9b2b1e68576ca83bcdefa4
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