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
Training dataset confirmation
#2
by CShorten - opened
Thank you so much for this model, so incredibly useful! I am citing this particular model and would like to know what dataset it was trained on -- from the paper I am uncertain wether this was trained with all datasets or solely with the paraphrase datasets -- i.e. MRPC. Thanks again!
You can find the info here:
https://www.sbert.net/examples/training/paraphrases/README.html
Thanks, really appreciate it!