Sentence Similarity
sentence-transformers
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use Youmnaaaa/Semantic-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Youmnaaaa/Semantic-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Youmnaaaa/Semantic-model") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use Youmnaaaa/Semantic-model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Youmnaaaa/Semantic-model") model = AutoModel.from_pretrained("Youmnaaaa/Semantic-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bc814781d3c0f194dfb82f68b55346b3aeadc1a317c7f64c7398c09617c6442a
- Size of remote file:
- 1.18 MB
- SHA256:
- b05cc94419dbb12b0c6e1158ed7a95a1f00a06c3d200a26f44ff99952bb088cd
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