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
Upload README.md with huggingface_hub
Browse files
README.md
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# CRP Intent / Speech-Act Classifier
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license: other
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pipeline_tag: text-classification
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library_name: setfit
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base_model: sentence-transformers/all-MiniLM-L6-v2
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tags:
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- setfit
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- sentence-transformers
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- text-classification
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- crp
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- context-relay-protocol
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- intent-classification
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- speech-acts
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widget:
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- text: Please scan the repository for compliance issues.
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- text: What is the current deployment status?
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- text: I believe the server is down.
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- text: This is frustrating and slow.
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inference: true
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model-index:
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- name: crp-intent-setfit
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results:
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- task:
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type: text-classification
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name: Speech-act classification (4-class)
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dataset:
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name: CRP speech-act held-out mix
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type: banking77
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metrics:
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- type: accuracy
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value: 0.934
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name: Held-out accuracy (2,000 examples)
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verified: false
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---
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# CRP Intent / Speech-Act Classifier
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