multiclinner_enigma_cz_disease_robeczech-morph

Czech clinical Named Entity Recognition model for the DISEASE entity type, fine-tuned from ufal/robeczech-base on the Czech portion of the MultiClinAI 2026 IberLEF shared task. Developed by Team Enigma at the Faculty of Mathematics and Informatics, Sofia University.

Summary

Task	Token classification (BIO), single entity type
Entity type	DISEASE (diseases and disorders)
Language	Czech (cs)
Base model	ufal/robeczech-base
Architecture	Transformer (softmax)
Augmentation	Morphological synonyms (curated + Wikidata)
Training data	MultiClinAI Czech train + dev combined (1,258 documents) plus augmentation
Test F1 (strict)	0.6552 (char F1 0.7737)

Quick start

from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

repo = "SU-FMI-AI/multiclinner_enigma_cz_disease_robeczech-morph"

tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForTokenClassification.from_pretrained(repo)

ner = pipeline(
    "token-classification",
    model=model,
    tokenizer=tokenizer,
    aggregation_strategy="simple",
)

text = "Pacient byl přijat s hypertenzí a podstoupil koronarografii."
for span in ner(text):
    print(span)

Each predicted span is a dictionary with keys entity_group, score, word, start, end.

Label set

The model predicts a single entity type using the BIO tagging scheme:

ID	Label	Meaning
0	O	outside any entity
1	B-DISEASE	beginning of a DISEASE mention
2	I-DISEASE	inside a DISEASE mention

Intended use

• Extracting DISEASE mentions from Czech clinical text (discharge summaries, case reports, medical records).
• Building block for ensembles (this model was deployed as part of an ensemble in the original submission).
• A starting point for further fine-tuning on related Czech biomedical corpora.

Out-of-scope use

• Other languages. Although the XLM-RoBERTa based variants share a multilingual encoder, the classification head was only trained on Czech.
• Other domains. The model was not exposed to non-clinical text, social media or layperson descriptions.
• Clinical decision making. This is a research artifact. Do not use it as the sole input to any clinical decision.

Training data

• Source. MultiClinAI Czech NER: 1,006 train documents and 252 dev documents per entity type in BRAT standoff format, derived from the DisTEMIST, SympTEMIST and MedProcNER corpora translated and annotation-projected to Czech. For the final submission models the gold train + dev sets are merged into a single training partition (no held-out validation).
• Augmentation. Morphological synonym replacement. Rare entity surface forms (occurring at most 5 times in training) are substituted with morphological variants drawn from a curated Czech medical synonym dictionary. The dictionary combines manually curated morphological paradigms for Czech medical terms with Wikidata concept labels for Czech medical entities, totalling roughly 1,400 synonym entries across about 900 morphological families. Approximately 2,200 augmented documents per entity type are appended to the 1,258 document gold train+dev partition.
• Tokenisation. SentencePiece tokenizer inherited from the base model.

Training procedure

Hyperparameter	Value
Base model	ufal/robeczech-base
Head	SOFTMAX
Optimiser	AdamW
Learning rate	2e-5
Batch size	64
Epochs	10
Max sequence length	512
Input granularity	Sentence-level
Warmup ratio	0.1
Weight decay	0.01
Seed	42
Mixed precision	fp16 (CUDA)

Token classification head: backbone hidden states are projected through a linear layer to BIO logits and decoded greedily (argmax per token).

Evaluation

Held-out development set

Best dev-set entity-level F1 observed during development: 0.698.

MultiClinAI Czech, official blind test set

Run name in the official MultiClinAI ranking: robeczech-morph_cz_disease.

Metric	Strict	Character-level
Precision	0.6741	0.7967
Recall	0.6373	0.7520
F1	0.6552	0.7737

Strict matching requires the predicted span to exactly match a gold span (same start, end, and type). Character-level matching gives partial credit for overlapping spans.

Related models

Other models for the same entity type:

• SU-FMI-AI/multiclinner_enigma_cz_disease_robeczech-para: robeczech-base, Morphological synonyms + GPT-4.1-mini paraphrase, SOFTMAX head, test F1 = 0.6494.
• SU-FMI-AI/multiclinner_enigma_cz_disease_xlmr-crf: xlm-roberta-base, Morphological synonyms (curated + Wikidata), CRF head, test F1 = 0.6381.
• SU-FMI-AI/multiclinner_enigma_cz_disease_xlmr-para: xlm-roberta-base, Morphological synonyms + GPT-4.1-mini paraphrase, SOFTMAX head, test F1 = 0.6283.
• SU-FMI-AI/multiclinner_enigma_cz_disease_xlmr-os1: xlm-roberta-base, Morphological synonyms + 1x oversample of entity-bearing docs, SOFTMAX head, test F1 = 0.6440.

License

Released under the apache-2.0 license. Base-model and dataset licenses apply to their respective artifacts.

Code and resources

Training code, augmentation pipeline, ablation log and evaluation scripts are available in the project's GitHub repository: https://github.com/TeogopK/MultiClinAI-Czech.