Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use alfaneo/bert-base-multilingual-sts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use alfaneo/bert-base-multilingual-sts with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("alfaneo/bert-base-multilingual-sts") 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 alfaneo/bert-base-multilingual-sts with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("alfaneo/bert-base-multilingual-sts") model = AutoModel.from_pretrained("alfaneo/bert-base-multilingual-sts", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from alfaneo/bert-base-multilingual-sts: direct link, hf CLI and curl.
- Browser
- Download file 711 MB
-
https://huggingface.co/alfaneo/bert-base-multilingual-sts/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://alfaneo/bert-base-multilingual-sts/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/alfaneo/bert-base-multilingual-sts/resolve/main/pytorch_model.bin
711 MB
- Xet hash:
- 4e0a684dfd0ca4f683f9dc640d1431277613a5fd9019045b3faa3c35e3a46939
- Size of remote file:
- 711 MB
- SHA256:
- b52b45885b22d0fe0bf625e0a9127ea76e64a9d1c3f6b4d9bc2cbc992f68c9d7
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