Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use sentence-transformers/paraphrase-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/paraphrase-MiniLM-L6-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/paraphrase-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/paraphrase-MiniLM-L6-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/paraphrase-MiniLM-L6-v2") model = AutoModel.from_pretrained("sentence-transformers/paraphrase-MiniLM-L6-v2") - Inference
- Notebooks
- Google Colab
- Kaggle
How do I reduce the size of sentence-transformers?
#5
by deejaypepe - opened
When I tried to deploy with docker, the layer is 4.7GB. How do I reduce to be less than 4GB?
It seems the dependencies are taking up the most. How do I know which one is necessary for a certain model?
If you're not using a GPU, then you can install torch with CPU only before installing sentence-transformers. E.g. try out this widget and elect CPU as the Compute Platform: https://pytorch.org/get-started/locally/
- Tom Aarsen