Instructions to use datamatters24/attuned-resonance-intake with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use datamatters24/attuned-resonance-intake with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="datamatters24/attuned-resonance-intake")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("datamatters24/attuned-resonance-intake", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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# Attuned Resonance Intake — Multi-Head RoBERTa for Call-Center Intake
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Six-head classifier/regressor built on `roberta-base`. Given a call transcript, emits a structured intake record used downstream by the [outcome predictor](https://huggingface.co/datamatters24/
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**Research/educational use only.** See disclaimer below.
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# Attuned Resonance Intake — Multi-Head RoBERTa for Call-Center Intake
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Six-head classifier/regressor built on `roberta-base`. Given a call transcript, emits a structured intake record used downstream by the [outcome predictor](https://huggingface.co/datamatters24/attuned-resonance-predictor) and the [PPO router](https://huggingface.co/datamatters24/attuned-resonance-router).
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> The GitHub repo slug is still "CEPM" during the gradual rename; the Hugging Face slugs were migrated to `attuned-resonance-*` on 2026-05-09 (HF preserves the old `cepm-*` URLs as redirects).
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**Research/educational use only.** See disclaimer below.
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