Sentence Similarity
sentence-transformers
PyTorch
Transformers
distilbert
feature-extraction
text-embeddings-inference
Instructions to use mrp/simcse-model-distil-m-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mrp/simcse-model-distil-m-bert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mrp/simcse-model-distil-m-bert") 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 mrp/simcse-model-distil-m-bert with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("mrp/simcse-model-distil-m-bert") model = AutoModel.from_pretrained("mrp/simcse-model-distil-m-bert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4b91ce316cd581750e5724e0f572627ba759be32c0f43cd4cf92d2dd554f01c8
- Size of remote file:
- 539 MB
- SHA256:
- 62558b22d2a017feaddf31ec565c3296d2ab3bf5c5f89c90cbab3ec18837b09e
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