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
| { | |
| "backend": "tokenizers", | |
| "boi_token": "<start_of_image>", | |
| "bos_token": "<bos>", | |
| "clean_up_tokenization_spaces": false, | |
| "eoi_token": "<end_of_image>", | |
| "eos_token": "<eos>", | |
| "image_token": "<image_soft_token>", | |
| "is_local": false, | |
| "local_files_only": false, | |
| "mask_token": "<mask>", | |
| "model_max_length": 256, | |
| "model_specific_special_tokens": { | |
| "boi_token": "<start_of_image>", | |
| "eoi_token": "<end_of_image>", | |
| "image_token": "<image_soft_token>" | |
| }, | |
| "pad_token": "<pad>", | |
| "padding_side": "right", | |
| "sp_model_kwargs": null, | |
| "spaces_between_special_tokens": false, | |
| "tokenizer_class": "GemmaTokenizer", | |
| "unk_token": "<unk>", | |
| "use_default_system_prompt": false | |
| } | |