Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
dense
Generated from Trainer
dataset_size:138
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Shaurya2020/allpanel-api-minilm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Shaurya2020/allpanel-api-minilm with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Shaurya2020/allpanel-api-minilm") sentences = [ "allpanelexch9 auth 2fa 2fa", "POST https://allpanelexch9.co/api/front/2fa (auth_2fa)", "POST https://allpanelexch9.co/api/front/userdata (account)", "POST https://allpanelexch9.co/api/front/authoff (auth_2fa)" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download 2_Normalize/config.json from Shaurya2020/allpanel-api-minilm: direct link, hf CLI and curl.
- Browser
- Download file 97 Bytes
-
https://huggingface.co/Shaurya2020/allpanel-api-minilm/resolve/main/2_Normalize/config.json
- Command line
-
hf download hf://Shaurya2020/allpanel-api-minilm/2_Normalize/config.json
-
curl -L -o config.json https://huggingface.co/Shaurya2020/allpanel-api-minilm/resolve/main/2_Normalize/config.json
97 Bytes
| { | |
| "module_input_name": "sentence_embedding", | |
| "module_output_name": "sentence_embedding" | |
| } |