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
- Xet hash:
- a8ff1a37310d6a5bda4a81e2c8653653d98d65fc01176dcba6bdbc0ebf2b3fe8
- Size of remote file:
- 90.9 MB
- SHA256:
- 5d716de760acbdc09e79a11e718c5606e0812b6aeb76c6664cba876d174e3ecd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.