Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
Transformers
distilbert
feature-extraction
text-embeddings-inference
Instructions to use sentence-transformers/quora-distilbert-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/quora-distilbert-multilingual with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/quora-distilbert-multilingual") 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/quora-distilbert-multilingual with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/quora-distilbert-multilingual") model = AutoModel.from_pretrained("sentence-transformers/quora-distilbert-multilingual", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sentence-transformers/quora-distilbert-multilingual: direct link, hf CLI and curl.
- Browser
- Download file 539 MB
-
https://huggingface.co/sentence-transformers/quora-distilbert-multilingual/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://sentence-transformers/quora-distilbert-multilingual@refs/pr/2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sentence-transformers/quora-distilbert-multilingual/resolve/refs%2Fpr%2F2/pytorch_model.bin
539 MB
- Xet hash:
- ea29f932bb3800792e9e2f16c782a1016f5936f5a8b28f735c5de979674850e4
- Size of remote file:
- 539 MB
- SHA256:
- 2a9b43deff48b39cb5720f8ba664e69134b8cba3dac449d0feaf6e58d2513056
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.