Text Classification
setfit
Safetensors
sentence-transformers
bert
crp
context-relay-protocol
intent-classification
speech-acts
Eval Results (legacy)
text-embeddings-inference
Instructions to use AutoCyberAI/crp-intent-setfit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use AutoCyberAI/crp-intent-setfit with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("AutoCyberAI/crp-intent-setfit") - sentence-transformers
How to use AutoCyberAI/crp-intent-setfit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AutoCyberAI/crp-intent-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] - Notebooks
- Google Colab
- Kaggle
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# CRP Intent / Speech-Act Classifier
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A SetFit sentence-transformer classifier that maps a user turn into one of four CRP speech acts: `request`, `question`, `assertion`, or `expressive`. Trained on Banking77, SNIPS, and synthetic CRP-style templates. Used by `crp.isa.intent` to decide how a turn should be routed and framed in the positioned agent loop.
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# CRP Intent / Speech-Act Classifier
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A SetFit sentence-transformer classifier that maps a user turn into one of four CRP speech acts: `request`, `question`, `assertion`, or `expressive`. Trained on Banking77, SNIPS, and synthetic CRP-style templates. Used by `crp.isa.intent` to decide how a turn should be routed and framed in the positioned agent loop.
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