Add precomputed exploration gallery, form selectors, full width, and UZH typography
aafe903 verified | title: Neural Transducer Inflection | |
| emoji: 🔤 | |
| colorFrom: blue | |
| colorTo: indigo | |
| sdk: gradio | |
| sdk_version: 5.48.0 | |
| app_file: app.py | |
| pinned: false | |
| license: cc-by-sa-4.0 | |
| short_description: Multilingual inflection with neural transducers | |
| # Neural Transducer: Morphological Inflection Demo | |
| An interactive demonstration of transition-based neural string transduction (Makarov & Clematide, 2020) trained with imitation learning. | |
| - **Benchmark:** CoNLL-SIGMORPHON 2017 Shared Task 1 (English, German, French, Italian) | |
| - **Regimes:** 100, 1,000, and 10,000 training examples | |
| - **Features:** Precomputed test gallery, live custom inputs, greedy decoding, and edit action inspection (`COPY`, `DELETE`, `INSERT`, `SUBSTITUTE`). | |
| ## Exploration gallery | |
| The Explore tab starts with precomputed predictions for 320 held-out test items | |
| (80 per language, excluding unchanged forms). Filter by model disagreement, | |
| improvement from 100 to 10,000 examples, largest-model errors, or all-correct | |
| cases. Selected cases illustrate behavior; they do not estimate accuracy. | |
| Custom words and random test samples run live. | |
| Regenerate from the local bundles after changing model weights: | |
| ```bash | |
| python scripts/precompute_examples.py --per-language 80 | |
| ``` | |
| Run that command from the repository root with the project dependencies installed. | |
| The JSON records a deterministic sampling seed and SHA-256 fingerprints of each | |
| local bundle, plus predictions and action traces. Commit the regenerated JSON | |
| with model updates. No models are loaded to browse the gallery. | |
| Custom inputs offer language-specific part-of-speech / word-form selectors, | |
| then readable grammatical-form choices. These use the unique full feature | |
| bundles already precomputed from the 10,000-example training split in | |
| `data_index.json`; no unobserved combinations are synthesized. Participles | |
| and converbs are grouped consistently even when the dataset also includes a | |
| `V` tag. Manual UniMorph entry remains available. Regenerate the data index | |
| with `python scripts/build_data_index.py` when the training data changes. | |