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--- |
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license: mit |
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language: |
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- sa |
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tags: |
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- sanskrit |
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- morphology |
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- prakriya |
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- vidyut |
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- generative-grammar |
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- sequence-to-sequence |
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--- |
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# Vidyut-Prakriya Tinanta Dataset for Morphological Rendering |
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This dataset contains pairs of Sanskrit morphological metadata and their corresponding surface forms, |
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_generated_ and _verified_ using the `vidyut-prakriya` library. |
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## Dataset Structure |
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The dataset is provided in JSON Lines (`.jsonl`) format. Each line is a JSON object with the following fields: |
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- `llm_input` (string): A textual representation of the morphological metadata. This serves as the input for a sequence-to-sequence LLM tasked with morphological rendering. |
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Example: `"Dhātu: BU (BvAdi), Lakāra: la~w, Prayoga: kartari, Puruṣa: praTama, Vacana: eka"` |
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- `surface_form_vidyut` (string): The Sanskrit surface form derived by `vidyut-prakriya` for the given metadata. |
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Example: `"Bavati"` |
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## Generation Process |
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1. **Dhātu Lexicon**: Dhātus (verb roots) are sourced from the `dhatupatha.tsv` provided with `vidyut-prakriya` (version 0.4.0 data). |
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2. **Metadata Combination**: For each dhātu, combinations of the following morphological features are generated: |
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- `Lakāra` (tense/mood) |
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- `Prayoga` (voice: kartari, karmani, bhave) |
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- `Puruṣa` (person: prathama, madhyama, uttama) |
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- `Vacana` (number: eka, dvi, bahu) |
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3. **Derivation & Verification**: The `vidyut.prakriya.Vyakarana.derive()` method is used to generate the surface form for each metadata combination. Only combinations that yield a valid surface form are included in the dataset. |
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4. **LLM Input Format**: The `llm_input` string is formatted to be human-readable and suitable for sequence-to-sequence models. Enum values (Lakāra, Prayoga, etc.) are represented by their SLP1 strings (e.g., `la~w` for `Lakāra.Lat`). |
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## Intended Use |
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This dataset is primarily intended for training and evaluating language models on the task of Sanskrit morphological rendering (i.e., generating a surface form from its underlying grammatical specification). |
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It can also be used for: |
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- Analyzing the coverage of `vidyut-prakriya`. |
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- Studies in Sanskrit computational linguistics. |
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## Project Context |
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This dataset was generated as part of a project inspired by Rohan Pandey's call for RL projects for Sanskrit. The goal is to use this data to train a model for morphological rendering and subsequently evaluate its impact on English-to-Sanskrit translation quality. |
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## Citation |
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If you use this dataset, please consider citing the `vidyut-prakriya` library and/or this repository (once created). |
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``` |
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