---
license: mit # Or choose another appropriate license, e.g., cc-by-sa-4.0
language:
  - sa # Sanskrit
tags:
  - sanskrit
  - morphology
  - prakriya
  - vidyut
  - generative-grammar
  - sequence-to-sequence
---

# Vidyut-Prakriya Tinanta Dataset for Morphological Rendering

This dataset contains pairs of Sanskrit morphological metadata and their corresponding surface forms,
_generated_ and _verified_ using the `vidyut-prakriya` library.

## Dataset Structure

The dataset is provided in JSON Lines (`.jsonl`) format. Each line is a JSON object with the following fields:

- `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.
  Example: `"Dhātu: BU (BvAdi), Lakāra: la~w, Prayoga: kartari, Puruṣa: praTama, Vacana: eka"`
- `surface_form_vidyut` (string): The Sanskrit surface form derived by `vidyut-prakriya` for the given metadata.
  Example: `"Bavati"`

## Generation Process

1.  **Dhātu Lexicon**: Dhātus (verb roots) are sourced from the `dhatupatha.tsv` provided with `vidyut-prakriya` (version 0.4.0 data).
2.  **Metadata Combination**: For each dhātu, combinations of the following morphological features are generated:
    - `Lakāra` (tense/mood)
    - `Prayoga` (voice: kartari, karmani, bhave)
    - `Puruṣa` (person: prathama, madhyama, uttama)
    - `Vacana` (number: eka, dvi, bahu)
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.
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`).

## Intended Use

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).

It can also be used for:
- Analyzing the coverage of `vidyut-prakriya`.
- Studies in Sanskrit computational linguistics.

## Project Context

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.

## Citation

If you use this dataset, please consider citing the `vidyut-prakriya` library and/or this repository (once created).

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