Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
text-embeddings-inference
Instructions to use yosshstd/ProTrek_650M_UniRef50_text_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yosshstd/ProTrek_650M_UniRef50_text_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yosshstd/ProTrek_650M_UniRef50_text_encoder") 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] - Notebooks
- Google Colab
- Kaggle
File size: 133 Bytes
3951c2a | 1 2 3 4 5 6 | {
"in_features": 768,
"out_features": 1024,
"bias": true,
"activation_function": "torch.nn.modules.linear.Identity"
} |