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:
# 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
- Xet hash:
- e6a6f409edf80927cc14153365925b9eeade0cd3b850bef37f7738d838199ce8
- Size of remote file:
- 1.89 GB
- SHA256:
- e7be21b1f0d10a8e5e7567b9e5b83a586a7dfe7704738bc0b362a0422aff93c4
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