Text Classification
Transformers
ONNX
Safetensors
English
distilbert
intent-classification
multitask
iab
conversational-ai
adtech
calibrated-confidence
text-embeddings-inference
Instructions to use admesh/agentic-intent-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use admesh/agentic-intent-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="admesh/agentic-intent-classifier")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("admesh/agentic-intent-classifier", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 736 Bytes
0584798 2b0e686 0584798 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | import json
try:
from .model_runtime import get_head # type: ignore
except ImportError:
from model_runtime import get_head
def predict(text: str, confidence_threshold: float | None = None):
return get_head("decision_phase").predict(text, confidence_threshold=confidence_threshold)
examples = [
"What is CRM software?",
"What are some CRM options for startups?",
"HubSpot vs Zoho for a small team",
"Which CRM should I buy for a 3-person startup?",
"Start my free trial",
"How do I set up my new CRM?",
"I cannot log into my account",
]
if __name__ == "__main__":
for text in examples:
print(f"\nInput: {text}")
print("Prediction:", json.dumps(predict(text), indent=2))
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