---
dataset_info:
  pretty_name: Vyakaran
  creators: 
    - Sanskrit Datasets
  license: Apache-2.0
  language:
    - sa-Deva
  size_categories: 100K<n<1M
  task_categories:
    - token-classification
    - language-modeling
    - text-generation
  tags:
    - sanskrit
    - tokenization
    - morphology
    - classical-literature
---

# Vyakaran  
**616 k blazing Sanskrit sentences—43 classics to supercharge your NLP**

`vyakaran.csv` distils two‑and‑a‑half millennia of Sanskrit wisdom into an analysis‑ready corpus: every line is a sentence from a public‑domain classic, paired with the whitespace‑separated tokens that emerge when you roll back *sandhi* and compounds. The result is a dependable springboard for tokenisers, morphological taggers, and large‑language‑model pre‑training.

---

## At a Glance  

| Metric | Value |
| ------ | ----- |
| Rows (sentence–token pairs) | **616 082** |
| Unique sentences | 607 713 |
| Unique token strings | 604 319 |
| File size | 323 MB (UTF‑8 CSV) |
| Columns | `index` · `sanskrit` · `tokens` |
| Licence | Apache 2.0 |

---

## ✨ Dataset Summary  

> *Because Sanskrit is precision disguised as poetry.*  
> Vyakaran gathers verses, dialogue, and narrative from 43 classics—*Mahābhārata*, *Śiva Purāṇa*, *Tantrāloka*, *Buddha‑Carita*, and more—then hands you both the raw sentence and a token string you can feed straight to SentencePiece or a CRF tagger. Use it to **train segmenters, pre‑train transformers, benchmark morphology**, or just explore the linguistic art of the ancients.

---

## Data Fields  

| Column | Type | Description |
| ------ | ---- | ----------- |
| `index` | `int32` | 0‑based row identifier (primary key). |
| `sanskrit` | `string` | Original sentence in Devanāgarī, NFC‑normalised UTF‑8. |
| `tokens` | `string` | Whitespace‑separated morphological tokens generated by a rule‑based Pāṇinian splitter. |

---



##  Quick Start  

```python
from datasets import load_dataset
ds = load_dataset("snskrt/Vyakaran", split="train")

print(ds[0]["sanskrit"])
# नन्दति लोकः सुकृतैः ...
tokens = ds[0]["tokens"].split()

```
## Bibetext
```
@misc{sanskrit_datasets_2025,
  author    = {Sanskrit Datasets},
  title     = {Vyakaran (Revision ca374f9)},
  year      = {2025},
  url       = {https://huggingface.co/datasets/snskrt/Vyakaran},
  doi       = {10.57967/hf/6073},
  publisher = {Hugging Face}
}

```