Sentence Similarity
sentence-transformers
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Transformers
English
bert
feature-extraction
Eval Results
text-embeddings-inference
Instructions to use sentence-transformers/all-MiniLM-L6-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/all-MiniLM-L6-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-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] - Transformers
How to use sentence-transformers/all-MiniLM-L6-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2") model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2") - Inference
- Notebooks
- Google Colab
- Kaggle
License and commercial usage
#34
by simkes - opened
Hello!
The model is under the apache-2.0 license, however, training data contains some datasets like MS MARCO and GooAQ, that are not allowed for commercial usage. Could someone clarify this?
Hi @simkes . We are also facing exactly this issue while using this model (and similar sentence-transformers models) in commercial applications. Were you able to find another sentence embedding model, which is commerical-friendly and is trained on data which is also commercial-friendly? Any guidance in this regard would be useful. Thank you.