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
| #!/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() | |