HiLabs clinical term mapping engine — trained models

Trained artifacts for the HiLabs AI Quest 2026 term-mapping engine (clinical entity + chart context → ICD-10-CM and SNOMED CT). Code, instructions and write-up: the GitHub repository linked in the submission.

The engine downloads this repository automatically into models/ on the first python run.py predict.

Path	Contents
`biencoder/`, `biencoder_r1/`	SapBERT bi-encoders fine-tuned contrastively (round 2 / round 1 hard negatives)
`crossencoder/`	SapBERT cross-encoder reranker (term + chunk context vs candidate)
`llm_adapter/`	QLoRA adapter for `unsloth/Qwen3-4B-Instruct-2507-bnb-4bit` (listwise reranker)
`r1_`, `r2_`, `r2l_`, `r3_` `__bag*.txt`	5-seed bagged LightGBM LambdaRank rankers; `ranker_features.json` lists their features
`ctx_*.pkl`	chunk-context concept-type classifiers (vocabulary stored as hashes, no plain chart text)
`pairs.csv`	ICD↔SNOMED code co-occurrence pairs (codes only)

No training data, chart text, dictionaries or API keys are included. Base models: SapBERT (cambridgeltl/SapBERT-from-PubMedBERT-fulltext, Apache-2.0) and Qwen3-4B-Instruct-2507 (Apache-2.0).

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