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
| from __future__ import annotations | |
| import argparse | |
| import json | |
| import sys | |
| from pathlib import Path | |
| BASE_DIR = Path(__file__).resolve().parent.parent | |
| if str(BASE_DIR) not in sys.path: | |
| sys.path.insert(0, str(BASE_DIR)) | |
| from config import EVALUATION_ARTIFACTS_DIR, IAB_BEHAVIOR_LOCK_CASES_PATH | |
| from evaluation.regression_suite import evaluate_iab_behavior_lock_cases | |
| def main() -> None: | |
| parser = argparse.ArgumentParser(description="Run behavior-lock IAB content regression checks.") | |
| parser.add_argument( | |
| "--cases-path", | |
| default=str(IAB_BEHAVIOR_LOCK_CASES_PATH), | |
| help="Structured behavior-lock IAB case file to execute.", | |
| ) | |
| parser.add_argument( | |
| "--output-dir", | |
| default=str(EVALUATION_ARTIFACTS_DIR / "latest"), | |
| help="Directory to write regression artifacts into.", | |
| ) | |
| args = parser.parse_args() | |
| summary = evaluate_iab_behavior_lock_cases(Path(args.cases_path), Path(args.output_dir)) | |
| print(json.dumps(summary, indent=2)) | |
| if __name__ == "__main__": | |
| main() | |