Sentence Similarity
sentence-transformers
PyTorch
Transformers
distilbert
feature-extraction
text-embeddings-inference
Instructions to use NimaBoscarino/STPushToHub-test2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NimaBoscarino/STPushToHub-test2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NimaBoscarino/STPushToHub-test2") 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/STPushToHub-test2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("NimaBoscarino/STPushToHub-test2") model = AutoModel.from_pretrained("NimaBoscarino/STPushToHub-test2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7153cf5f3e68d123aa1e77c1365b62ea913ae1bec10971e94cf62bb70b817fc6
- Size of remote file:
- 265 MB
- SHA256:
- d6c85675b41d2694b728ce387fe056cca56a9c4158270b49c42ce62a0aac1de6
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