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 reader/encoder/model.safetensors from Secnd-AI/ren: direct link, hf CLI and curl.
- Browser
- Download file 1.58 GB
-
https://huggingface.co/Secnd-AI/ren/resolve/main/reader/encoder/model.safetensors
- Command line
-
hf download hf://Secnd-AI/ren/reader/encoder/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Secnd-AI/ren/resolve/main/reader/encoder/model.safetensors
1.58 GB
- Xet hash:
- 8a209cb2c689331eda7ebc978a59d1c99c80176929a9c0ee696ded0d7123ca2b
- Size of remote file:
- 1.58 GB
- SHA256:
- 5396a0be1ab8bbca221641c61018ab62528f9884ddcabf65d3599d8824f8d949
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