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: 759 Bytes
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 28 | #!/usr/bin/env python3
"""End-to-end production pipeline entry point (Colab-friendly).
Runs ``training/run_full_training_pipeline.py`` and forwards CLI args.
Typical:
python complete_pipeline.py --complete
python complete_pipeline.py --skip-full-eval --complete # Colab: skip heavy eval suites
python training/pipeline_verify.py # only check artifacts
"""
from __future__ import annotations
import subprocess
import sys
from pathlib import Path
BASE_DIR = Path(__file__).resolve().parent
def main() -> None:
script = BASE_DIR / "training" / "run_full_training_pipeline.py"
cmd = [sys.executable, str(script), *sys.argv[1:]]
subprocess.run(cmd, cwd=BASE_DIR, check=True)
if __name__ == "__main__":
main()
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