Red Flag Detection: modifier module (Qwen2.5-3B-Instruct + LoRA merged)
Part of a 5-module medical red flag detection system for Brunei English (Manglish), Chinese, and Bahasa Melayu clinical notes / patient messages.
This model is the modifier extraction module — one of 5 specialized modules used
together with a Python rule engine (V20 spec, 59 rules).
Sister modules
peiyan-ning/redflag-symptom-3b— 83-symptom multi-label extractionpeiyan-ning/redflag-context-3b— 12 context flags (post_trauma, drowning, etc.)peiyan-ning/redflag-modifier-3b— onset / fever_celsius / consciousness / etc.peiyan-ning/redflag-denied-3b— denied symptoms (multi-turn negation)peiyan-ning/redflag-gate-3b— 8 population gates (is_pregnant, is_child, ...)
Performance (2246-case independent test set)
Full 5-module pipeline + rule engine V46:
| Metric | P | R | F1 | Acc |
|---|---|---|---|---|
| PRIMARY (any_matched × labeled_matched) | 0.902 | 0.911 | 0.906 | 91.9% |
| STRICT matched-only | 0.893 | 0.828 | 0.859 | 91.8% |
| STRICT m+s | 0.844 | 0.905 | 0.873 | 92.1% |
System prompt used at inference
Extract quantitative modifiers from text.
===== EXTRACT onset =====
Set onset:"acute" when text describes SUDDEN event:
- "suddenly", "just", "out of nowhere", "in an instant", "突然", "tiba-tiba"
- Trauma events: "fell", "crash", "hit"
- Symptom onset described as fast: "just started"
Set onset:"chronic" when described as long-standing/gradual.
===== EXTRACT temperature =====
When text has actual number: "39.5°C" / "38 degrees" / "40度" → fever_celsius
===== EXTRACT other modifiers =====
- seizure duration in minutes → seizure_duration_min
- inhaler used / worked → inhaler_used / inhaler_effective
- bleeding amount described "heavy" / "uncontrolled" → bleeding_severity
- burn size → burn_severity
- burn location → burn_location
- consciousness: alert / confused / drowsy / unresponsive
===== EXAMPLES =====
"Suddenly severe chest pain" → {"modifiers": {"onset": "acute"}}
"Fell down and hit head" → {"modifiers": {"onset": "acute"}}
"Fever 39.5 for 3 days" → {"modifiers": {"onset": "chronic", "fever_celsius": 39.5, "fever_days": 3}}
"Seizure lasted 7 minutes" → {"modifiers": {"seizure_duration_min": 7}}
"Uncontrolled bleeding" → {"modifiers": {"bleeding_severity": "uncontrolled"}}
"Not sure about symptoms" → {"modifiers": {}}
Output: {"modifiers": {...}}
===== MULTILINGUAL / MANGLISH GUIDANCE =====
Text may be in Brunei/Manglish English or mixed with Malay/Chinese.
Ignore these colloquial particles when extracting: "lah", "kah", "meh", "ah", "leh", "lor", "sia", "one".
Common Manglish/Malay/Chinese mappings:
- "kena panic attack" / "feel like dying" / "jantung deg-deg" → severe_panic
- "sesak nafas" (Malay) / "喘不过气" → breathlessness
- "sakit dada" (Malay) / "胸口疼" → chest_pain
- "sakit kepala teruk" / "剧烈头痛" / "worst headache" → thunderclap_headache
- "pengsan" (Malay) / "晕倒" → fainting
- "sawan" (Malay) / "抽搐" → seizure
- "anak saya" (Malay: my child) → is_child
- "bayi saya" (Malay: my baby) → is_baby
- "warga emas" / "老人家" → is_elderly
- "hamil" / "怀孕" → is_pregnant
- "kencing manis" (Malay: diabetes) → has_diabetes
- "asma" (Malay: asthma) → has_asthma
- "kena patuk ular" (Malay: snake bit) → context_flags: venomous_bite
Auntie/uncle in Manglish family reference: usually elderly family member → is_elderly.
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch, json
tok = AutoTokenizer.from_pretrained('peiyan-ning/redflag-modifier-3b')
model = AutoModelForCausalLM.from_pretrained(
'peiyan-ning/redflag-modifier-3b',
torch_dtype=torch.float16,
device_map='auto'
)
SYSTEM_PROMPT = tok.chat_template # or use the prompt above
messages = [
{'role': 'system', 'content': SYSTEM_PROMPT},
{'role': 'user', 'content': 'My 3-year-old child has severe fever and vomiting lah'},
]
inputs = tok.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors='pt').to(model.device)
out = model.generate(inputs, max_new_tokens=200, do_sample=False)
text = tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True)
result = json.loads(text)
print(result)
Full pipeline
See git.evyd.tech/ai/redflag-detection-2.0 for:
- Rule engine (59 V20 rules)
- Post-processing (gate_detector, severity_extractor, numeric_extractor)
- End-to-end sample inference code
Training
- Base: Qwen/Qwen2.5-3B-Instruct
- LoRA: r=32, α=64, dropout=0.05
- Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- 2 epochs, LR 2e-4, cosine, warmup 5%, effective batch 32
- Multi-lingual: EN/ZH/MS with Manglish particles (lah/kah/meh)