Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
text-embeddings-inference
Instructions to use yahyaabd/bps-custom-tokenizer-paraphrase-multilingual-MiniLM-L12-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yahyaabd/bps-custom-tokenizer-paraphrase-multilingual-MiniLM-L12-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yahyaabd/bps-custom-tokenizer-paraphrase-multilingual-MiniLM-L12-v2") 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] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 33208f939f3383ebb5e8577f60f21241989a63dd9bd6ffd26fe87c79493b8271
- Size of remote file:
- 17.3 MB
- SHA256:
- a01705e9d44f0213e0e03180eefe22d9d5b2569db1deb917f92dcc6749976a10
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