Sentence Similarity
sentence-transformers
Transformers
English
rk-transformers
bert
feature-extraction
rknn
rockchip
npu
rk3588
text-embeddings-inference
Instructions to use rk-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 rk-transformers/all-MiniLM-L6-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("rk-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 rk-transformers/all-MiniLM-L6-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("rk-transformers/all-MiniLM-L6-v2") model = AutoModel.from_pretrained("rk-transformers/all-MiniLM-L6-v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f708d757835753f44d7d81b6a5d00ea7b64e801474b5558837d4e4364d51a1dd
- Size of remote file:
- 51.3 MB
- SHA256:
- 4eae8ea4b3dd8bd2332da540b52a0f8707cf5f890fc31bd91d221ca1d85de64c
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