--- license: cc-by-4.0 language: - el pretty_name: MORFES size_categories: - n<1K annotations_creators: - expert-generated language_creators: - expert-generated source_datasets: - original task_categories: - multiple-choice - text-generation tags: - morphology - inflection - modern-greek - greek - linguistics - evaluation configs: - config_name: default data_files: - split: test path: morfes.csv ---
Sophea AI
MORFES
Benchmark for Productive Inflectional Competence in Modern Greek
**MORFES** (Morphological Open-class Recognition-and-Formation Evaluation Suite; also μορφές, Greek for "forms") is a benchmark of 500 expert-verified items testing productive inflectional competence in Modern Greek. - **Task:** Modern Greek inflectional morphology (recognition and production) - **Languages:** Greek - **Size:** 500 items - **License:** CC-BY-4.0
Resources
MORFES is introduced and described in the accompanying paper.
Sophea-Genesis-1, an open Greek language model, is released alongside it.
On MORFES it is the most accurate of the open models we evaluated.
📄 Read the paper (arXiv) 🤖 Sophea-Genesis-1 (model)
Given a lemma (dictionary form) and a grammatical specification, each item probes whether a model can recognize and produce the correct inflected form. MORFES favors lower-frequency lemmas so that a correct answer reflects the productive rule rather than a memorized form. The full construct, item selection, frequency control, and evaluation are described in the accompanying paper. ## Categories The 500 items span six categories: the three richly inflecting open classes (noun, adjective, verb), each in a single-form and a full-paradigm variant. | Category | Items | |---|---:| | Noun declension: single form | 50 | | Noun declension: full paradigm | 50 | | Adjective declension: single form | 50 | | Adjective declension: full paradigm | 50 | | Verb conjugation: single form | 150 | | Verb conjugation: full paradigm | 150 | A single-form item asks for one cell of a paradigm (for example the genitive singular of a noun); a full-paradigm item asks for a complete sub-table (for example all six persons of a verb tense). The set is verb-weighted, with 300 of the 500 items verbs: the verb is the most richly inflecting of the three, with far more distinct forms than a noun or adjective, so an equal split would under-sample the open class that inflects the most. ## Data fields One row per item, 10 columns: | Column | Description | |---|---| | `id` | Stable item identifier, for example `gr-00001`. | | `category` | One of the six categories above. | | `question` | The item prompt, in Greek. | | `A`, `B`, `C`, `D` | The four candidate forms. Their order is randomized per item. | | `answer` | The letter (`A` to `D`) of the correct option. | | `accepted_answers` | The correct surface form(s), as a JSON string: a flat list for single-form items (for example `["θέρισαν", "θερίσανε"]`, the aorist third-person plural of «θερίζω», "to reap"), and a list of per-cell lists for full-paradigm items. Greek often admits more than one correct form for a single specification. | | `lemma` | The base (dictionary) form the item is built on. One distinct lemma per item. | The file is a UTF-8 CSV; standard loaders read it directly: ```python from datasets import load_dataset ds = load_dataset("KIEFERSA/MORFES", split="test") ``` ## Contamination control Each item's `lemma` is provided so users can hold these lemmas out of a model's training data and guard against contamination. Every item is built on a distinct lemma, and the requested form is never the lemma's dictionary form, so no item can be answered by repeating a word from the prompt. ## Citation If you use MORFES, please cite: ```bibtex @misc{perros2026morfes, title = {{MORFES}: A Benchmark for Productive Inflectional Competence in Modern Greek}, author = {Perros, Ioakeim and Papadopoulou, Cleopatra and Kirouane, Ayoub and Petrocheilos, Christos}, year = {2026}, howpublished = {\url{https://huggingface.co/datasets/KIEFERSA/MORFES}}, note = {arXiv:2607.28274} } ``` ## License MORFES is released under the [Creative Commons Attribution 4.0 International (CC-BY-4.0)](https://creativecommons.org/licenses/by/4.0/) license. You may share and adapt the data, including for commercial use, provided you give appropriate attribution; please attribute by citing the paper above.