Sentence Similarity
sentence-transformers
PyTorch
Transformers
distilbert
feature-extraction
text-embeddings-inference
Instructions to use NimaBoscarino/July25Test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NimaBoscarino/July25Test with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NimaBoscarino/July25Test") 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 NimaBoscarino/July25Test with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("NimaBoscarino/July25Test") model = AutoModel.from_pretrained("NimaBoscarino/July25Test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5d5115fba794a3c9482a4f1f3f0dcbff701de6dc39f084b0f5918b49297f4a18
- Size of remote file:
- 265 MB
- SHA256:
- 3fb2216abc8acefc30416bc18c7e993f119ce82428c60895640d23f33583c4c4
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