Sentence Similarity
sentence-transformers
Safetensors
Transformers
PyTorch
English
gemma3_text
feature-extraction
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use Surpem/Supertron-embedding-300M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Surpem/Supertron-embedding-300M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Surpem/Supertron-embedding-300M") 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 Surpem/Supertron-embedding-300M with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Surpem/Supertron-embedding-300M") model = AutoModel.from_pretrained("Surpem/Supertron-embedding-300M") - Notebooks
- Google Colab
- Kaggle
File size: 677 Bytes
10601fb | 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 32 | [
{
"idx": 0,
"name": "0",
"path": "",
"type": "sentence_transformers.base.modules.transformer.Transformer"
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{
"idx": 1,
"name": "1",
"path": "1_Pooling",
"type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
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{
"idx": 2,
"name": "2",
"path": "2_Dense",
"type": "sentence_transformers.base.modules.dense.Dense"
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{
"idx": 3,
"name": "3",
"path": "3_Dense",
"type": "sentence_transformers.base.modules.dense.Dense"
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{
"idx": 4,
"name": "4",
"path": "4_Normalize",
"type": "sentence_transformers.sentence_transformer.modules.normalize.Normalize"
}
] |