Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use sentence-transformers/use-cmlm-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/use-cmlm-multilingual with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/use-cmlm-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/use-cmlm-multilingual with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/use-cmlm-multilingual") model = AutoModel.from_pretrained("sentence-transformers/use-cmlm-multilingual", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from sentence-transformers/use-cmlm-multilingual: direct link, hf CLI and curl.
- Browser
- Download file 9.62 MB
-
https://huggingface.co/sentence-transformers/use-cmlm-multilingual/resolve/main/tokenizer.json
- Command line
-
hf download hf://sentence-transformers/use-cmlm-multilingual/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/sentence-transformers/use-cmlm-multilingual/resolve/main/tokenizer.json
9.62 MB
File too large to display, you can check the raw version instead.