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
File size: 746 Bytes
0021448 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | {
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"architectures": [
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"is_decoder": false,
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"model_type": "bert",
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"num_hidden_layers": 12,
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}
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