Sentence Similarity
sentence-transformers
Safetensors
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use ingeol/q2d_origin_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ingeol/q2d_origin_5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ingeol/q2d_origin_5") 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 ingeol/q2d_origin_5 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ingeol/q2d_origin_5") model = AutoModel.from_pretrained("ingeol/q2d_origin_5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 569b14a31a5477eef6c25379f7a33ac700b6dd16c26702fd639d1b5ad0c9c6f1
- Size of remote file:
- 438 MB
- SHA256:
- 8cdd852cde78866355bd30f12207d448f8730eec5c55e27c4e28cf4049e9ab27
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