Add dataset card (README.md)

README.md

@@ -0,0 +1,55 @@
+---
+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).
+
+```