TermMap-DX trained artifacts

README.md

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+---
+license: apache-2.0
+language: en
+tags:
+  - medical
+  - clinical-coding
+  - icd-10-cm
+  - snomed-ct
+  - entity-linking
+  - learning-to-rank
+---
+
+# TermMap-DX: trained artifacts
+
+Trained models for **TermMap-DX**, an engine that maps clinical terms extracted from charts to
+one ICD-10-CM code and one SNOMED CT concept (HiLabs Hackathon 2026). The code lives in the team's
+GitHub repository, [Shresth-Negi/TermMap-DX](https://github.com/Shresth-Negi/TermMap-DX) (private;
+judges have access). There, `scripts/download_artifacts.py` pulls these files into place, and
+`scripts/run_all.sh` does so automatically before mapping an input file:
+
+```bash
+git clone https://github.com/Shresth-Negi/TermMap-DX.git && cd TermMap-DX
+# set up the environment as in the README, put the challenge files in data/raw/, then
+bash scripts/run_all.sh <input.csv> outputs/predictions.csv   # downloads these artifacts first
+```
+
+## Contents
+
+| files | what |
+|---|---|
+| `ce_ICD10CM_d3e4/`, `ce_SNOMEDCT_US_d3e4/` | cross-encoders fine-tuned from `cambridgeltl/SapBERT-from-PubMedBERT-fulltext` (4 epochs, listwise loss over candidate shortlists) |
+| `ranker_<system>_s1<set>.txt`, `ranker_<system>_s2<set>.txt` (+ `.json` feature lists) | LightGBM LambdaRank models, stage 1 and stage 2, per coding system |
+| `calib_<system><set>.json` | isotonic calibration (ranker margin -> P(correct)) |
+| `config<set>.json` | model-set configuration: encoders, cross-encoder, LLM, fallback set |
+| `emb_<encoder>_<system>.npy` | float16 embeddings of every unique dictionary description (row = description id rebuilt by the code from the supplied dictionaries) for SapBERT, gte-base and Qwen3-Embedding-0.6B |
+| `manifest.json` | sha256 and size of every file |
+
+Model sets: `_d3llm14e4` (default; adds Qwen2.5-14B-Instruct scores at inference), `_d3llm7e4`
+(Qwen2.5-7B-Instruct), `_d3ce4` (no LLM), `_sap4` (SapBERT only, lightest). The LLMs and encoders
+are not included; they are pulled from their public Hugging Face repositories.
+
+## Results (5-fold cross-validation on the training file)
+
+| set | ICD-10-CM exact match | SNOMED CT exact match |
+|---|---|---|
+| `_d3llm14e4` | 0.708 | 0.582 |
+| `_d3llm7e4` | 0.702 | 0.567 |
+| `_d3ce4` | 0.684 | 0.543 |
+| `_sap4` | 0.658 | 0.507 |
+
+## Training data and licences
+
+Trained on the HiLabs hackathon training file (proprietary; not included here). The supplied
+ICD-10-CM and SNOMED CT US dictionaries define the answer space; the embedding matrices are
+derived from their descriptions, so use is subject to the SNOMED CT licence terms that apply to
+you. Base models: SapBERT (Apache-2.0), gte-base (MIT), Qwen3-Embedding-0.6B (Apache-2.0),
+Qwen2.5-7B/14B-Instruct (Apache-2.0, used at inference only).

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ranker_ICD10CM_s1_d3ce4.json

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+{"cols": ["char_max", "word_max", "char_var", "sapbert_max", "sapbert_pref", "sapbert_var", "reading_ctx", "reading_ctx_gap", "ctx_dense", "gte_max", "gte_pref", "gte_var", "qwen3_max", "qwen3_pref", "qwen3_var", "num_match", "num_conflict", "cand_neg", "cand_hist", "neg_match", "hist_match", "fz_ratio", "fz_tset", "fz_partial", "fz_tsort", "tok_qcov", "tok_dcov", "tok_dextra", "ctx_overlap", "cand_lat", "lat_match", "lat_conflict", "lat_missing", "nn_cat_char_max", "nn_cat_char_sum", "nn_cat_dense_max", "nn_cat_dense_sum", "nn_cat_cnt", "nn_char_max", "nn_char_sum", "nn_dense_max", "nn_dense_sum", "nn_cnt", "nn_entctx_max", "nn_ctx_max", "prior", "exact", "mention", "n_desc", "billable", "code_len", "chapter", "block_prior", "is_unspec", "is_other", "enc7", "char_max_rank", "char_max_gap", "word_max_rank", "word_max_gap", "char_var_rank", "char_var_gap", "sapbert_max_rank", "sapbert_max_gap", "sapbert_var_rank", "sapbert_var_gap", "gte_max_rank", "gte_max_gap", "gte_var_rank", "gte_var_gap", "qwen3_max_rank", "qwen3_max_gap", "qwen3_var_rank", "qwen3_var_gap", "fz_tset_rank", "fz_tset_gap", "nn_cat_char_max_rank", "nn_cat_char_max_gap", "nn_cat_dense_max_rank", "nn_cat_dense_max_gap", "nn_char_max_rank", "nn_char_max_gap", "nn_dense_max_rank", "nn_dense_max_gap", "nn_entctx_max_rank", "nn_entctx_max_gap", "nn_ctx_max_rank", "nn_ctx_max_gap", "prior_rank", "prior_gap", "q_len", "q_ntok", "q_digit", "q_abbr", "q_nvar", "q_lat", "q_neg", "q_hist", "q_fam", "subconcept", "n_cand"]}

ranker_ICD10CM_s1_d3llm14e4.json

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+{"cols": ["char_max", "word_max", "char_var", "sapbert_max", "sapbert_pref", "sapbert_var", "reading_ctx", "reading_ctx_gap", "ctx_dense", "gte_max", "gte_pref", "gte_var", "qwen3_max", "qwen3_pref", "qwen3_var", "num_match", "num_conflict", "cand_neg", "cand_hist", "neg_match", "hist_match", "fz_ratio", "fz_tset", "fz_partial", "fz_tsort", "tok_qcov", "tok_dcov", "tok_dextra", "ctx_overlap", "cand_lat", "lat_match", "lat_conflict", "lat_missing", "nn_cat_char_max", "nn_cat_char_sum", "nn_cat_dense_max", "nn_cat_dense_sum", "nn_cat_cnt", "nn_char_max", "nn_char_sum", "nn_dense_max", "nn_dense_sum", "nn_cnt", "nn_entctx_max", "nn_ctx_max", "prior", "exact", "mention", "n_desc", "billable", "code_len", "chapter", "block_prior", "is_unspec", "is_other", "enc7", "char_max_rank", "char_max_gap", "word_max_rank", "word_max_gap", "char_var_rank", "char_var_gap", "sapbert_max_rank", "sapbert_max_gap", "sapbert_var_rank", "sapbert_var_gap", "gte_max_rank", "gte_max_gap", "gte_var_rank", "gte_var_gap", "qwen3_max_rank", "qwen3_max_gap", "qwen3_var_rank", "qwen3_var_gap", "fz_tset_rank", "fz_tset_gap", "nn_cat_char_max_rank", "nn_cat_char_max_gap", "nn_cat_dense_max_rank", "nn_cat_dense_max_gap", "nn_char_max_rank", "nn_char_max_gap", "nn_dense_max_rank", "nn_dense_max_gap", "nn_entctx_max_rank", "nn_entctx_max_gap", "nn_ctx_max_rank", "nn_ctx_max_gap", "prior_rank", "prior_gap", "q_len", "q_ntok", "q_digit", "q_abbr", "q_nvar", "q_lat", "q_neg", "q_hist", "q_fam", "subconcept", "n_cand"]}

ranker_ICD10CM_s1_d3llm7e4.json

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ranker_ICD10CM_s1_sap4.json

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ranker_ICD10CM_s2_d3ce4.json

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ranker_ICD10CM_s2_d3llm14e4.json

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ranker_ICD10CM_s2_d3llm7e4.json

@@ -0,0 +1 @@
+{"cols": ["char_max", "word_max", "char_var", "sapbert_max", "sapbert_pref", "sapbert_var", "reading_ctx", "reading_ctx_gap", "ctx_dense", "gte_max", "gte_pref", "gte_var", "qwen3_max", "qwen3_pref", "qwen3_var", "num_match", "num_conflict", "cand_neg", "cand_hist", "neg_match", "hist_match", "fz_ratio", "fz_tset", "fz_partial", "fz_tsort", "tok_qcov", "tok_dcov", "tok_dextra", "ctx_overlap", "cand_lat", "lat_match", "lat_conflict", "lat_missing", "nn_cat_char_max", "nn_cat_char_sum", "nn_cat_dense_max", "nn_cat_dense_sum", "nn_cat_cnt", "nn_char_max", "nn_char_sum", "nn_dense_max", "nn_dense_sum", "nn_cnt", "nn_entctx_max", "nn_ctx_max", "prior", "exact", "mention", "n_desc", "billable", "code_len", "chapter", "block_prior", "is_unspec", "is_other", "enc7", "char_max_rank", "char_max_gap", "word_max_rank", "word_max_gap", "char_var_rank", "char_var_gap", "sapbert_max_rank", "sapbert_max_gap", "sapbert_var_rank", "sapbert_var_gap", "gte_max_rank", "gte_max_gap", "gte_var_rank", "gte_var_gap", "qwen3_max_rank", "qwen3_max_gap", "qwen3_var_rank", "qwen3_var_gap", "fz_tset_rank", "fz_tset_gap", "nn_cat_char_max_rank", "nn_cat_char_max_gap", "nn_cat_dense_max_rank", "nn_cat_dense_max_gap", "nn_char_max_rank", "nn_char_max_gap", "nn_dense_max_rank", "nn_dense_max_gap", "nn_entctx_max_rank", "nn_entctx_max_gap", "nn_ctx_max_rank", "nn_ctx_max_gap", "prior_rank", "prior_gap", "q_len", "q_ntok", "q_digit", "q_abbr", "q_nvar", "q_lat", "q_neg", "q_hist", "q_fam", "subconcept", "n_cand", "s1", "s1_rank", "s1_p", "s1_gap", "xs_cooc", "xs_cooc_top1", "xs_sim", "xs_sim_top1", "llm_lp", "llm_lp_rank", "llm_lp_gap", "ce", "ce_lp", "ce_rank", "ce_gap", "ce_lp_rank", "ce_lp_gap"]}

ranker_ICD10CM_s2_sap4.json

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+{"cols": ["char_max", "word_max", "char_var", "sapbert_max", "sapbert_pref", "sapbert_var", "reading_ctx", "reading_ctx_gap", "ctx_dense", "num_match", "num_conflict", "cand_neg", "cand_hist", "neg_match", "hist_match", "fz_ratio", "fz_tset", "fz_partial", "fz_tsort", "tok_qcov", "tok_dcov", "tok_dextra", "ctx_overlap", "cand_lat", "lat_match", "lat_conflict", "lat_missing", "nn_cat_char_max", "nn_cat_char_sum", "nn_cat_dense_max", "nn_cat_dense_sum", "nn_cat_cnt", "nn_char_max", "nn_char_sum", "nn_dense_max", "nn_dense_sum", "nn_cnt", "nn_entctx_max", "nn_ctx_max", "prior", "exact", "mention", "n_desc", "billable", "code_len", "chapter", "block_prior", "is_unspec", "is_other", "enc7", "char_max_rank", "char_max_gap", "word_max_rank", "word_max_gap", "char_var_rank", "char_var_gap", "sapbert_max_rank", "sapbert_max_gap", "sapbert_var_rank", "sapbert_var_gap", "fz_tset_rank", "fz_tset_gap", "nn_cat_char_max_rank", "nn_cat_char_max_gap", "nn_cat_dense_max_rank", "nn_cat_dense_max_gap", "nn_char_max_rank", "nn_char_max_gap", "nn_dense_max_rank", "nn_dense_max_gap", "nn_entctx_max_rank", "nn_entctx_max_gap", "nn_ctx_max_rank", "nn_ctx_max_gap", "prior_rank", "prior_gap", "q_len", "q_ntok", "q_digit", "q_abbr", "q_nvar", "q_lat", "q_neg", "q_hist", "q_fam", "subconcept", "n_cand", "s1", "s1_rank", "s1_p", "s1_gap", "xs_cooc", "xs_cooc_top1", "xs_sim", "xs_sim_top1"]}

ranker_SNOMEDCT_US_s1_d3ce4.json

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+{"cols": ["char_max", "word_max", "char_var", "sapbert_max", "sapbert_pref", "sapbert_var", "reading_ctx", "reading_ctx_gap", "ctx_dense", "gte_max", "gte_pref", "gte_var", "qwen3_max", "qwen3_pref", "qwen3_var", "num_match", "num_conflict", "cand_neg", "cand_hist", "neg_match", "hist_match", "fz_ratio", "fz_tset", "fz_partial", "fz_tsort", "tok_qcov", "tok_dcov", "tok_dextra", "ctx_overlap", "cand_lat", "lat_match", "lat_conflict", "lat_missing", "nn_char_max", "nn_char_sum", "nn_dense_max", "nn_dense_sum", "nn_cnt", "nn_entctx_max", "nn_ctx_max", "prior", "exact", "mention", "n_desc", "semtag", "best_order", "best_is_pref", "char_max_rank", "char_max_gap", "word_max_rank", "word_max_gap", "char_var_rank", "char_var_gap", "sapbert_max_rank", "sapbert_max_gap", "sapbert_var_rank", "sapbert_var_gap", "gte_max_rank", "gte_max_gap", "gte_var_rank", "gte_var_gap", "qwen3_max_rank", "qwen3_max_gap", "qwen3_var_rank", "qwen3_var_gap", "fz_tset_rank", "fz_tset_gap", "nn_char_max_rank", "nn_char_max_gap", "nn_dense_max_rank", "nn_dense_max_gap", "nn_entctx_max_rank", "nn_entctx_max_gap", "nn_ctx_max_rank", "nn_ctx_max_gap", "prior_rank", "prior_gap", "q_len", "q_ntok", "q_digit", "q_abbr", "q_nvar", "q_lat", "q_neg", "q_hist", "q_fam", "subconcept", "n_cand"]}

ranker_SNOMEDCT_US_s1_d3llm14e4.json

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+{"cols": ["char_max", "word_max", "char_var", "sapbert_max", "sapbert_pref", "sapbert_var", "reading_ctx", "reading_ctx_gap", "ctx_dense", "gte_max", "gte_pref", "gte_var", "qwen3_max", "qwen3_pref", "qwen3_var", "num_match", "num_conflict", "cand_neg", "cand_hist", "neg_match", "hist_match", "fz_ratio", "fz_tset", "fz_partial", "fz_tsort", "tok_qcov", "tok_dcov", "tok_dextra", "ctx_overlap", "cand_lat", "lat_match", "lat_conflict", "lat_missing", "nn_char_max", "nn_char_sum", "nn_dense_max", "nn_dense_sum", "nn_cnt", "nn_entctx_max", "nn_ctx_max", "prior", "exact", "mention", "n_desc", "semtag", "best_order", "best_is_pref", "char_max_rank", "char_max_gap", "word_max_rank", "word_max_gap", "char_var_rank", "char_var_gap", "sapbert_max_rank", "sapbert_max_gap", "sapbert_var_rank", "sapbert_var_gap", "gte_max_rank", "gte_max_gap", "gte_var_rank", "gte_var_gap", "qwen3_max_rank", "qwen3_max_gap", "qwen3_var_rank", "qwen3_var_gap", "fz_tset_rank", "fz_tset_gap", "nn_char_max_rank", "nn_char_max_gap", "nn_dense_max_rank", "nn_dense_max_gap", "nn_entctx_max_rank", "nn_entctx_max_gap", "nn_ctx_max_rank", "nn_ctx_max_gap", "prior_rank", "prior_gap", "q_len", "q_ntok", "q_digit", "q_abbr", "q_nvar", "q_lat", "q_neg", "q_hist", "q_fam", "subconcept", "n_cand"]}

ranker_SNOMEDCT_US_s1_d3llm7e4.json

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+{"cols": ["char_max", "word_max", "char_var", "sapbert_max", "sapbert_pref", "sapbert_var", "reading_ctx", "reading_ctx_gap", "ctx_dense", "gte_max", "gte_pref", "gte_var", "qwen3_max", "qwen3_pref", "qwen3_var", "num_match", "num_conflict", "cand_neg", "cand_hist", "neg_match", "hist_match", "fz_ratio", "fz_tset", "fz_partial", "fz_tsort", "tok_qcov", "tok_dcov", "tok_dextra", "ctx_overlap", "cand_lat", "lat_match", "lat_conflict", "lat_missing", "nn_char_max", "nn_char_sum", "nn_dense_max", "nn_dense_sum", "nn_cnt", "nn_entctx_max", "nn_ctx_max", "prior", "exact", "mention", "n_desc", "semtag", "best_order", "best_is_pref", "char_max_rank", "char_max_gap", "word_max_rank", "word_max_gap", "char_var_rank", "char_var_gap", "sapbert_max_rank", "sapbert_max_gap", "sapbert_var_rank", "sapbert_var_gap", "gte_max_rank", "gte_max_gap", "gte_var_rank", "gte_var_gap", "qwen3_max_rank", "qwen3_max_gap", "qwen3_var_rank", "qwen3_var_gap", "fz_tset_rank", "fz_tset_gap", "nn_char_max_rank", "nn_char_max_gap", "nn_dense_max_rank", "nn_dense_max_gap", "nn_entctx_max_rank", "nn_entctx_max_gap", "nn_ctx_max_rank", "nn_ctx_max_gap", "prior_rank", "prior_gap", "q_len", "q_ntok", "q_digit", "q_abbr", "q_nvar", "q_lat", "q_neg", "q_hist", "q_fam", "subconcept", "n_cand"]}