Instructions to use zikabyte/garda-setfit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use zikabyte/garda-setfit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("zikabyte/garda-setfit") 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] - setfit
How to use zikabyte/garda-setfit with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("zikabyte/garda-setfit") - Notebooks
- Google Colab
- Kaggle
GARDA - SetFit Grooming Risk Classifier
Model Description
This is a SetFit model fine-tuned from
sentence-transformers/all-MiniLM-L6-v2
to flag indications of online predatory grooming in English-language chat
messages. It is a binary classifier (label 1 = suspicious, 0 = normal)
trained on short conversation windows (a message plus the few messages
before it, tagged self/other) rather than isolated single lines.
This model is a triage aid, not a verdict. See "Bias, Risks and Limitations" below before using it for anything beyond a demo/research context.
Training Data
Fine-tuned on the PAN-2012 Sexual Predator Identification corpus, a restricted-access, non-redistributable dataset of real chat logs used under permission for academic research. No excerpts of the raw dataset are included in this model card or repository - only the resulting model weights are shared, consistent with the dataset's usage terms.
Uses
Direct Use for Inference
from setfit import SetFitModel
model = SetFitModel.from_pretrained("zikabyte/garda-setfit")
probas = model.predict_proba([
"self: hey\nother: hi\nother: how old are you?",
])
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Model tree for zikabyte/garda-setfit
Base model
nreimers/MiniLM-L6-H384-uncased