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:
- 62b706e67cd4356eff956517f268fcb6aa9a04b9d97ffd9ec236324e837a0535
- Size of remote file:
- 32.7 kB
- SHA256:
- a51e6b74d082bdc05eb0776b2b6ee4d6ee422d41f2b13213a673957ee1f923be
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