Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use osanseviero/test_sentence_transformers2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use osanseviero/test_sentence_transformers2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("osanseviero/test_sentence_transformers2") 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 osanseviero/test_sentence_transformers2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("osanseviero/test_sentence_transformers2") model = AutoModel.from_pretrained("osanseviero/test_sentence_transformers2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fe56021a4f9e77967fbaaa631cb0a5845174b5076a2fd8f56f53b21d4ade773f
- Size of remote file:
- 69.6 MB
- SHA256:
- 26841357258de045f20358894414b88e39386b13bd99831596cece9d8512bf1f
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