| ---
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| language: [zh]
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| license: mit
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| tags: [conceptual-metaphor, annotation, lakoff, parallel-corpus, lora, qwen2.5]
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| base_model: Qwen/Qwen2.5-1.5B-Instruct
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| ---
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|
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| > **⚠️ Terms of use — research only, non-commercial, no redistribution.**
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| > These models were trained on UM-Corpus sentences (Tian et al., LREC 2014; CC BY-NC-ND 4.0)
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| > **with written permission from NLP2CT, University of Macau** (on file, 2026-11-11).
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| > By downloading you agree: (1) academic, non-commercial use only; (2) no redistribution of
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| > the weights; (3) attribution in any resulting publication to UM-Corpus (NLP2CT, University
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| > of Macau) and to CM-Annotator. Full provenance: see docs/DATA_CARD.md in the code repo.
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|
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| # CM-Annotator (Chinese) — Conceptual Metaphor Annotator
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| LoRA adapter that annotates Chinese sentences with conceptual metaphor spans
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| (Lakoff & Johnson tradition) over a curated closed-set inventory (215 entries, based on the
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| Master Metaphor List, Lakoff et al. 1991). Outputs strict JSON:
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| `{"has_metaphor": bool, "metaphor_spans": [{text, cm_id, cm_name, source_domain, target_domain,
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| conventionality, confidence}]}`. Sentences without conceptual metaphors yield empty spans
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| (Null-CM negatives are ~30% of training).
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|
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| ## Training (distillation)
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|
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| - Silver labels distilled from a local LM Studio teacher (qwen3.6-35b-a3b), k=2 self-consistency;
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| only agreeing labels kept. Details: `docs/TRAINING.md`. Silver labels are NOT gold-human labels.
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| - Base: `Qwen/Qwen2.5-1.5B-Instruct` · LoRA r=16 α=32 ·
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| completion-only loss · seed=42 · trained 2026-11-07T18:37:56.652602+00:00
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|
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| ## Evaluation (dev vs silver, greedy)
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|
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| | metric | value |
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| |---|---|
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| | n_eval | 100 |
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| | JSON valid rate | 1.0 |
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| | span precision / recall / F1 | 0.0357 / 0.037 / 0.0364 |
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|
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| ## Usage
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| Load base + adapter with peft; build prompts exactly as in `service/inference.py`
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| (system rules + INVENTORY block + SENTENCE). Trained-on data derives from UM-Corpus (CC BY-NC-ND):
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| training data is not redistributed; reproduce via the pipeline in the repository.
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|
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| ## Limitations
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| Precision bounded by teacher quality (see audit table in docs/TRAINING.md); recall conservative
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| on conventional metaphors; confidence not calibrated; Chinese side corpus is mostly simplified script.
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|
