Text Classification
sentence-transformers
Safetensors
English
bert
ren
secnd
selective-classification
calibration
human-in-the-loop
text-embeddings-inference
Instructions to use Secnd-AI/ren with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Secnd-AI/ren with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Secnd-AI/ren") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Download meta/scorer.pt from Secnd-AI/ren: direct link, hf CLI and curl.
- Browser
- Download file 4.21 MB
-
https://huggingface.co/Secnd-AI/ren/resolve/main/meta/scorer.pt
- Command line
-
hf download hf://Secnd-AI/ren/meta/scorer.pt
-
curl -L -o scorer.pt https://huggingface.co/Secnd-AI/ren/resolve/main/meta/scorer.pt
4.21 MB
- Xet hash:
- 026fcd96a296f7d16ef35e4ffd614a8a4e4e2977b2c159b8bd109d20b5630ec6
- Size of remote file:
- 4.21 MB
- SHA256:
- b4278574da0b2ac0004661cd6f7e3e9250c4bbacd6ab835c0a88cfab1dabb9c8
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