Restructure deployment model card and document project and checkpoint licenses

LICENSE

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+MIT License
+
+Copyright (c) 2026 Elias Dahl Lunøe & Lucas E. Bülow Mortensen
+
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+SOFTWARE.

LICENSE-APACHE

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README.md

@@ -1,62 +1,202 @@
 ---
 license: apache-2.0
+library_name: transformers
+pipeline_tag: text2text-generation
+base_model: google/flan-t5-base
 language:
   - en
   - nl
   - da
   - es
-  - fr
 tags:
-  - text2text-generation
   - icd
   - historical-demography
   - digital-humanities
-base_model: google/flan-t5-base
+  - multi-label
+  - text2text-generation
 ---
 
-# codLLM base (deployment checkpoint)
+# codLLM-base-deploy
+
+A fine-tuned **FLAN-T5-base** model that maps historical free-text causes of death to **ICD10h codes**.
+This is the deployment checkpoint from our June 2026 bachelor's thesis at the Technical University of Denmark (DTU).
+The main model series for the forthcoming publication will be released separately.
+
+[Thesis (PDF)][thesis] · [Source code][code] · [codLLM organization](https://huggingface.co/codllm)
+
+| Property | Description |
+|---|---|
+| Task | Sequence-to-sequence, multi-label ICD10h coding |
+| Base model | [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) |
+| Training input | Cause-of-death text, age, and sex |
+| Output | One or more comma-separated ICD10h codes |
+| Intended use | Historical mortality research with expert review |
+| Authors | Lucas E. B. Mortensen and Elias Dahl Lunøe |
+
+## Quick start
+
+Install the inference dependencies; Transformers 5.1.0 is the version recorded with this checkpoint:
+
+```bash
+uv add "transformers==5.1.0" torch sentencepiece
+```
+
+The repository is currently private. An account with access must first authenticate with `hf auth login`.
+
+```python
+import torch
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+model_id = "codllm/codllm-base-deploy"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
+model.eval()
+
+text = "cod: fever | age: unknown | sex: unknown"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
+
+with torch.inference_mode():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=32,
+        do_sample=False,
+        num_beams=1,
+    )
+
+prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)
+codes = [code.strip() for code in prediction.split(",") if code.strip()]
+print(codes)
+```
+
+This is a minimal CPU example with an illustrative input. It does not compute uncertainty or reproduce the thesis evaluation.
+The explicit generation limit overrides the bundled `generation_config.json` default of `max_length=20`.
+The training pipeline raises its target budget when needed for longer multi-code targets; check for truncation in such cases.
+
+## Input and output
 
-Fine-tuned FLAN-T5-base that assigns **ICD-10h** codes to free-text causes of
-death from historical death registers (roughly 1850-1939), across Dutch, Danish,
-Spanish, French, English and Latin phrasings with archaic spellings.
+Use the deployment training format, with all three fields in this order:
 
-## Input format matters, a lot
+```text
+cod: fever | age: unknown | sex: unknown
+```
 
-The model is highly sensitive to its input shape. Two formats were trained:
+- Preserve the `cod: `, `age: `, and `sex: ` prefixes and the ` | ` separators.
+- Use the literal `unknown` for missing or unmappable metadata; keep the field in the input.
+- Follow the project's [input preprocessing](https://github.com/murrodroid/codllm/blob/main/src/codllm/input/transform.py)
+  for age and sex normalization. The quick start assumes text has already been formatted.
+- COD-only inputs were studied in separate experiments. Their bare-string format should not be treated as
+  interchangeable with this checkpoint's metadata input or as reproducing its reported scores.
 
-| format | example |
+Predictions are comma-separated code strings. Evaluation treats codes as an unordered set.
+Use the included [ICD10h 2024 masterlist](./ICD10h_Masterlist_2024.tsv) for code descriptions and validity checks;
+free generation does not enforce membership in the masterlist.
+
+## Training and data
+
+The thesis describes 1,525,048 historical records from Amsterdam, Copenhagen, Madrid, Belgium, and Ipswich,
+plus English reference strings. Raw archival records are subject to a data-use agreement and are not distributed here.
+The model uses masterlist pretraining followed by historical fine-tuning.
+
+The project's [reviewed language inventory][languages] identifies Dutch (including Flemish/Belgian Dutch), Danish,
+Spanish, and English. The Belgian dataset owner's clarification supersedes the thesis's French/Dutch description.
+Historical Latin expressions can occur within records; this is not a separately evaluated Latin capability.
+
+<details>
+<summary>Deployment training recipe</summary>
+
+Settings below are reported in the thesis, Sections 6.1–6.2 and Table 6.4.
+
+| Setting | Value |
 |---|---|
-| `cod` | `tabes meseraica` |
-| `cod_age_sex` | `cod: stuipen \\| age: unknown \\| sex: male` |
+| Historical split | Random record-level 90/5/5; data seed 333 |
+| Input fields | COD + age + sex |
+| Rare-code upsampling floor | 200 |
+| Synthetic multi-COD ratio | 0.30 |
+| Base perturbation rate | 0.50 |
+| Learning rate / schedule | 2.5e-5 / constant with warmup |
+| Warmup ratio | 0.10 |
+| Effective batch size | 256 |
+| Maximum fine-tuning epochs | 80 |
+| Early-stopping patience | 10 epochs |
+| Checkpoint selection | Highest validation macro-F1 |
+| Source / target token budgets | 256 / 32 by default; target budget raised when necessary |
+
+These are the thesis recipe settings. Current publication experiment specifications describe later work.
+
+</details>
+
+## Evaluation
+
+The following are **thesis-reported results for codLLM-base-deploy**, Table 7.1, on historical records with
+English reference strings excluded. They have not been recomputed for this card update.
+
+| Metric | Full historical test | Unseen-string slice |
+|---|---:|---:|
+| Records | 76,249 | 5,862 |
+| Exact-match accuracy | 0.985 ± 0.001 | 0.843 ± 0.009 |
+| Jaccard | 0.989 ± 0.001 | 0.884 ± 0.007 |
+| Macro-F1 | 0.749 | 0.625 |
+| Micro-F1 | 0.987 ± 0.001 | 0.902 ± 0.006 |
+
+**Split context matters:** 92.19% of historical test records share their exact cause text with a training record
+(thesis Table 5.3). The unseen-string slice excludes cause strings appearing verbatim in training or validation.
+It is a subset of the same record-level split, not an independently grouped or source-held-out test.
+Rare causes and near-duplicate wording affect its interpretation.
+
+The ± values are 95% cluster-bootstrap half-widths over distinct input strings, using 5,000 resamples.
+They describe a fixed model and test set, not variation across training seeds. Macro-F1 is reported without an interval.
+
+The thesis's leave-one-source-out results use **five separately trained holdout checkpoints** (Section 7.4).
+They are not transfer scores for this uploaded checkpoint and do not establish cross-lingual performance.
+
+## Uncertainty and limitations
+
+The thesis ranks predictions by mean token entropy to study selective prediction.
+On the unseen-string slice, retaining the most confident 80% raises exact-match accuracy from 0.843 to 0.925
+(Figure 7.3). This is a rank-based result, not a calibrated probability or a universal entropy threshold.
+Evaluate any acceptance rule on representative target data before using it to defer records for review.
+
+- Performance depends on the archive's wording, label conventions, and representation in training.
+- Rare codes and unfamiliar cause strings are harder; a correct broad category can still have an incorrect sub-code.
+- Confident errors occur, and generated codes should be checked against the reference vocabulary.
+- Training labels come from human-coded registers and can contain errors. The thesis does not establish performance
+  across repeated training seeds or arbitrary new archives.
+
+Use the model as support for historical coding and expert review. Clinical diagnosis and contemporary medical coding
+were not evaluated.
 
-Rules that are easy to get wrong:
+## Citation
 
-* **cod-only is a bare string** with no `cod: ` prefix.
-* **cod_age_sex always emits all three fields**, using the literal string
-  `unknown` when a value is missing or unmappable. Omitting a field produces a
-  shape the model never saw in training.
+Lucas E. B. Mortensen and Elias Dahl Lunøe (2026).
+*Automated Historical Cause-of-Death Coding Using Fine-Tuned Language Models.*
+Bachelor's thesis, DTU Compute, Technical University of Denmark. [Read the thesis][thesis].
 
-Which format performs better is **source dependent**. On held-out sources scored
-by leave-one-source-out checkpoints, exact match ranged from 0.043 to 0.593 on
-the same records purely by changing the format. Measure on your own data rather
-than assuming a default.
+<details>
+<summary>BibTeX</summary>
 
-## Output
+```bibtex
+@thesis{mortensen_lunoe_2026_codllm,
+  author = {Mortensen, Lucas E. B. and Lunøe, Elias Dahl},
+  title = {Automated Historical Cause-of-Death Coding Using Fine-Tuned Language Models},
+  institution = {Technical University of Denmark},
+  type = {Bachelor's thesis},
+  year = {2026},
+  month = jun,
+  url = {https://huggingface.co/spaces/codllm/README/resolve/main/codLLM_Thesis.pdf}
+}
+```
 
-Comma-separated ICD-10h codes; the task is multi-label. Pair the model with the
-included `ICD10h_Masterlist_2024.tsv` for code descriptions.
+</details>
 
-## Uncertainty
+## License
 
-Mean token entropy over the generated sequence is a usable abstention signal.
-The reference operating point is `0.127`, chosen for roughly 80% coverage on an
-unseen-string slice. Entropy correlates with output length, so it is a better
-ranking signal than an absolute per-record probability.
+The [codLLM project source code][code] is licensed under the **MIT License**.
+An unchanged copy of the project's license, including the authors' copyright notice, is included as [LICENSE](./LICENSE).
 
-## Limitations
+This checkpoint retains its existing **Apache-2.0** license declaration; see [LICENSE-APACHE](./LICENSE-APACHE).
+It modifies Google's [FLAN-T5-base](https://huggingface.co/google/flan-t5-base) weights through codLLM fine-tuning.
+The project-code license does not replace the checkpoint license or grant rights to third-party archival data or reference materials.
 
-* Sub-code granularity may follow a different archive's coding convention than
-  yours; the broader category is often right where the sub-code differs.
-* On cause strings never seen in training, accuracy drops substantially.
-* Confident wrong codes occur, particularly for convention mismatches, so a
-  low-entropy prediction is not a guarantee of correctness.
+[thesis]: https://huggingface.co/spaces/codllm/README/resolve/main/codLLM_Thesis.pdf
+[code]: https://github.com/murrodroid/codllm
+[languages]: https://github.com/murrodroid/codllm/blob/main/data/curation/source_languages.toml