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calerio/silent-signals-rqc
silent-signals-rqc is a machine learning model from calerio. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Structured generator — produces (root, ingroup, definition, explanation) JSON. Flan-T5-XL + LoRA.
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Updated Apr 29, 2026
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From the Hugging Face model README
Structured generator — produces (root, ingroup, definition, explanation) JSON. Flan-T5-XL + LoRA.
Headline metric: ingroup.macro_f1 — ingroup macro-F1 on the grouped-split test (see docs/rq_c_report.md).
Each variant is checked into its own branch. Load with:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("calerio/silent-signals-rqc", revision="<branch>")
tok = AutoTokenizer.from_pretrained("calerio/silent-signals-rqc", revision="<branch>")
| Branch | Variant id | ingroup.macro_f1 | Notes |
|---|---|---|---|
patha-seed123 | rqc_pathA_seed123 | 0.3604 | — |
patha-seed42 | rqc_pathA_seed42 | 0.3829 | — |
patha-seed7 | rqc_pathA_seed7 | 0.3933 | default |
pathb-seed42 | rqc_pathB_seed42 | — | HPC-only / not yet pushed |
pathb-seed123 | rqc_pathB_seed123 | — | HPC-only / not yet pushed |
pathb-seed7 | rqc_pathB_seed7 | — | HPC-only / not yet pushed |
rqc_pathA_seed7 — see the per-task default_variant_rationale in data/manifests/model_inventory.json of the project repo.
Restricted to Path A (unbalanced) — the defensible system per docs/rq_c_report.md § 4. Path B variants stay in the dropdown for comparison but aren't the default.
Bocconi 597 NLP group project on dog-whistle detection and disambiguation, on the silent_signals corpus (Kruk et al. 2024). Full methodology: docs/DESIGN_DEFENSE.md + per-RQ reports in the project repo. HF Space build write-up: docs/hf_space_report.md.