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 1_Pooling/config.json from Shaurya2020/allpanel-api-minilm: direct link, hf CLI and curl.
- Browser
- Download file 90 Bytes
-
https://huggingface.co/Shaurya2020/allpanel-api-minilm/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://Shaurya2020/allpanel-api-minilm/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/Shaurya2020/allpanel-api-minilm/resolve/main/1_Pooling/config.json
90 Bytes
| { | |
| "embedding_dimension": 384, | |
| "pooling_mode": "mean", | |
| "include_prompt": true | |
| } |