Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use JoBeer/paraphrase-MiniLM-L6-v2-eclass with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use JoBeer/paraphrase-MiniLM-L6-v2-eclass with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("JoBeer/paraphrase-MiniLM-L6-v2-eclass") 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 JoBeer/paraphrase-MiniLM-L6-v2-eclass with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("JoBeer/paraphrase-MiniLM-L6-v2-eclass") model = AutoModel.from_pretrained("JoBeer/paraphrase-MiniLM-L6-v2-eclass", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 51738458b8a3fae6fc9070105905d9c3bc908fbc2345af39ab8001f92f31836d
- Size of remote file:
- 90.9 MB
- SHA256:
- 82115a2af2125a64abdfce65dddcd607a69a7b98562df9993c59c8550e907d8b
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