Sentence Similarity
sentence-transformers
PyTorch
TensorBoard
Transformers
English
German
bert
feature-extraction
Generated from Trainer
text-embeddings-inference
Instructions to use LLukas22/all-MiniLM-L12-v2-embedding-all with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LLukas22/all-MiniLM-L12-v2-embedding-all with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LLukas22/all-MiniLM-L12-v2-embedding-all") 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 LLukas22/all-MiniLM-L12-v2-embedding-all with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LLukas22/all-MiniLM-L12-v2-embedding-all") model = AutoModel.from_pretrained("LLukas22/all-MiniLM-L12-v2-embedding-all") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2e9b67716cc9abaddf5144974bb3ed5ad61b5d7a30e5714db1956a80871aa697
- Size of remote file:
- 134 MB
- SHA256:
- c0b3ec92623fe8d89d0a2c7abfef56a738ddb6d60546ec66609ef5add5c2f4bd
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