---
language:
- sa
- en
license:
- cc-by-4.0
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
task_categories:
- token-classification
- structured-prediction
- sequence-modeling
task_ids:
- part-of-speech-tagging
- lemmatization
- morphological-analysis
pretty_name: Sanskrit Morphological Sequence Corpus (Vidyut-Verified)
tags:
- sanskrit
- morphology
- vyakarana
- pāṇinian
- vidyut
- dcs
- universal-dependencies
- slp1
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: id
    dtype: string
  - name: tokens
    sequence: string
  - name: lemmas
    sequence: string
  - name: aupadeshika
    sequence: string
  - name: pos_tags
    sequence: string
  - name: vibhakti
    sequence: string
  - name: vacana
    sequence: string
  - name: purusha
    sequence: string
  - name: prayoga
    sequence: string
  - name: linga
    sequence: string
  - name: n_match
    dtype: int64
  - name: n_mismatch
    dtype: int64
  - name: n_unverified
    dtype: int64
  - name: verification
    dtype: string
  splits:
  - name: train
    num_bytes: 501686774
    num_examples: 710785
  download_size: 82377323
  dataset_size: 501686774
---

# Sanskrit Morphological Sequence Corpus (Vidyut-Verified)

A large-scale, **Pāṇinian-verified** morphological sequence dataset for
classical and Vedic Sanskrit. Every token is annotated with its lemma,
generative root (aupadeśika), part-of-speech, case, number, person, voice,
and gender — all in the SLP1 transliteration, and all aligned at the
sentence level for sequence-tagging / seq2seq training.

- **710,785 sentences** (after deduplication)
- **5,511,664 tokens**
- **14 columns** (10 linguistic + 4 Vidyut verification annotations)
- **Parquet format** (columnar, compressed)
- **Sources**: Digital Corpus of Sanskrit (DCS, CC BY 4.0) + Universal
  Dependencies Sanskrit treebanks (UD-Sanskrit-Vedic, UD-Sanskrit-UFAL;
  CC BY-SA 4.0)

---

## Dataset Summary

| | |
|---|---|
| Sentences | 710,785 |
| Tokens | 5,511,664 |
| Vocabulary (types) | ~30k lemmas |
| Transliteration | SLP1 (Sanskrit Library Phonetic Basic) |
| Schema per row | sentence-aligned sequences of token-level annotations |
| Verification | Vidyut 0.4.0 Pāṇinian round-trip checksum on every token |
| Format | Apache Parquet |
| License | CC BY 4.0 (DCS) / CC BY-SA 4.0 (UD) — see *Licensing* below |

### Token-level POS distribution

| Vidyut POS | Tokens | % | Meaning |
|---|---:|---:|---|
| `Subanta` | 3,542,853 | 64.3% | Declined nominals (nouns, pronouns, adjectives, numerals) |
| `Avyaya` | 1,002,920 | 18.2% | Indeclinables (adverbs, particles, conjunctions) |
| `Tinanta` | 520,321 | 9.4% | Finite verbs |
| `Krdanta` | 445,570 | 8.1% | Non-finite verb forms (participles, infinitives, gerunds, absolutives) |

---

## Schema (14 columns)

Each row is one sentence. All `Sequence` columns are equal-length, aligned
token-by-token.

### Linguistic columns (10)

| Column | Type | Description |
|---|---|---|
| `id` | `string` | Source sentence id (DCS occurrence id or UD `sent_id`) |
| `tokens` | `Sequence[string]` | SLP1 surface words (sandhi-split padāni) |
| `lemmas` | `Sequence[string]` | SLP1 clean lexical lemma (dictionary form) |
| `aupadeshika` | `Sequence[string]` | SLP1 generative root blueprint (accented, with prefixes/sanādi); for `Subanta`/`Avyaya` equals the lemma; for `Tinanta`/`Krdanta` resolved via the Vidyut Kosha (e.g. `kf` → `kf\Y`) |
| `pos_tags` | `Sequence[string]` | Vidyut POS: `Subanta` / `Tinanta` / `Krdanta` / `Avyaya` |
| `vibhakti` | `Sequence[string]` | Vidyut case (8 values) or `"None"` if inapplicable |
| `vacana` | `Sequence[string]` | Vidyut number: `eka` / `dvi` / `bahu`, or `"None"` |
| `purusha` | `Sequence[string]` | Vidyut person: `praTama` / `maDyama` / `uttama`, or `"None"` |
| `prayoga` | `Sequence[string]` | Vidyut voice: `kartari` / `karmaRi` / `BAve`, or `"None"` |
| `linga` | `Sequence[string]` | Vidyut gender: `puM` / `strI` / `napuMsaka`, or `"None"` |

### Verification columns (4)

| Column | Type | Description |
|---|---|---|
| `n_match` | `int64` | # tokens Vidyut generated and matched the surface form |
| `n_mismatch` | `int64` | # tokens Vidyut generated but did NOT match (likely ārṣa/Vedic irregularity or rare mapping edge-case) |
| `n_unverified` | `int64` | # tokens Vidyut could not attempt (engine coverage gap: pronoun sarvanāmasa, special `-ī` stems, some kṛt pratyayas) |
| `verification` | `string` | Categorical status (see *Verification status* below) |

### Null-padding convention

Features that don't apply to a given POS class are the literal string
`"None"` (not the Python `None`):

| POS class | `vibhakti` | `vacana` | `purusha` | `prayoga` | `linga` |
|---|---|---|---|---|---|
| `Subanta` (nominal) | ✓ | ✓ | None | None | ✓ |
| `Tinanta` (finite verb) | None | ✓ | ✓ | ✓ | None |
| `Krdanta` (participle) | ✓ | ✓ | None | ✓ | ✓ |
| `Avyaya` (indeclinable) | None | None | None | None | None |

### Vidyut enum value strings

The `vibhakti` / `vacana` / `purusha` / `prayoga` / `linga` columns use the
exact SLP1 strings Vidyut's `from_string` classmethods accept, so the
columns can be fed directly into `vidyut.prakriya.{Vibhakti,Vacana,Purusha,
Prayoga,Linga}.from_string(value)`:

- **vibhakti**: `praTamA`, `dvitIyA`, `tftIyA`, `caturTI`, `paYcamI`, `zazWI`, `saptamI`, `samboDanam`
- **vacana**: `eka`, `dvi`, `bahu`
- **purusha**: `praTama`, `maDyama`, `uttama`
- **prayoga**: `kartari`, `karmaRi`, `BAve`
- **linga**: `puM`, `strI`, `napuMsaka`

---

## Sources

### 1. Digital Corpus of Sanskrit (DCS) — primary

- URL: https://github.com/OliverHellwig/sanskrit
- Path in repo: `dcs/data/conllu/`
- Format: UD-compatible CoNLL-U, **IAST** transliteration
- Size: ~745k sentences / ~5.5M tokens, ~15,900 `.conllu` files
- Coverage: Ṛgveda, Atharvaveda (Śaunaka & Paippalāda), Mahābhārata, Rāmāyaṇa, major Upaniṣads, sūtras, Purāṇas, Buddhist Sanskrit, grammatical treatises, and more.
- License: **CC BY 4.0**
- Citation: *Oliver Hellwig, Digital Corpus of Sanskrit (DCS), 2010–2024.*

DCS is not web-scraped text. It is the product of over a decade of
computational-linguistic work by Dr. Oliver Hellwig and collaborators,
run through constraint-solvers and hand-corrected by Sanskrit experts.
It is currently the most rigorously peer-reviewed Sanskrit morphological
database in existence.

### 2. Universal Dependencies — Sanskrit treebanks

- **UD_Sanskrit-Vedic**: https://github.com/UniversalDependencies/UD_Sanskrit-Vedic
- **UD_Sanskrit-UFAL**: https://github.com/UniversalDependencies/UD_Sanskrit-UFAL
- Format: CoNLL-U, **IAST** transliteration
- License: **CC BY-SA 4.0**

UD rows are identifiable by a `_` in their `id` (e.g. `71508_1`); DCS rows
by a numeric occurrence id (e.g. `96540`).

---

## Construction Pipeline

The dataset was built by the `sanskrit_morpho` package:

1. **Fetch** — shallow-clone UD treebanks; sparse-checkout only
   `dcs/data/conllu/` from the DCS repo (no C++/R analysis code).
2. **Parse** — standard CoNLL-U parser; multi-word sandhi-fused range
   rows (e.g. `1-2 bhagavāñśrāvastyāṃ`) are dropped when the split
   padāni follow; purely fused blocks with no split forms are dropped.
3. **Normalize to SLP1** — all tokens and lemmas transliterated from IAST
   to SLP1. The terminal-anusvāra rule is applied per token: a trailing
   `M` (anusvāra) at word-end is normalized to the labial nasal `m`
   (e.g. `vanaM` → `vanam`); medial anusvāras are preserved (sandhi-relevant).
4. **Tag-map to Vidyut** — UD `UPOS` → Vidyut `Subanta/Tinanta/Avyaya/Krdanta`;
   UD `Case/Number/Person/Voice/Gender` → Vidyut
   `vibhakti/vacana/purusha/prayoga/linga`. Non-finite `VerbForm`
   (`Part`, `Inf`, `Ger`, `Conv`, `Abs`) overrides `Tinanta` → `Krdanta`.
5. **Null-pad** — inapplicable features set to the string `"None"`.
6. **Aupadeśika resolution** — for `Tinanta`/`Krdanta`, the clean SLP1
   lemma is looked up in the Vidyut Kosha (45k-entry dhatu index) and the
   metalanguage `aupadeshika` root (accented, with prefixes/sanādi) is
   stored in the `aupadeshika` column.
7. **QC drop (build-time)** — drop incomplete sentences (any token with
   missing POS or lemma) and purely-sandhi-fused blocks with no split
   forms. 1 row dropped at build time.
8. **Dedup + verify (post-build)** — exact-sentence deduplication +
   structural validation + Vidyut round-trip verification.

---

## Verification (Vidyut Round-Trip Checksum)

Every token was verified using [Vidyut 0.4.0](https://github.com/ambuda-org/vidyut)
(Ambuda's Pāṇinian engine) as a cryptographic checksum. The direction is
**backward** (the only direction Vidyut supports — it can generate but
cannot decompose):

1. **Read row**: pull a token's `aupadeshika` / `lemmas` + `pos_tags` +
   `vibhakti` / `vacana` / `purusha` / `prayoga` / `linga`.
2. **Generate**: construct a `vidyut.prakriya.Pada` and call
   `Vyakarana().derive()` to produce all Pāṇinian surface forms.
3. **Assert**: check if Vidyut's generated output contains the surface
   token from the dataset.

A per-token verdict of `match` / `mismatch` / `unverified` is recorded,
then aggregated per sentence into the `verification` column. Missing
features iterate over all possible enum values so partially-annotated
tokens (e.g. pronouns without gender) can still match.

### Verification status distribution

| `verification` | Sentences | % | Meaning |
|---|---:|---:|---|
| `full_match` | 35,500 | 5.0% | Every token Pāṇini-compliant — **verified strict gold** |
| `partial_match` | 124,056 | 17.5% | All verifiable tokens matched; some `unverified` (Vidyut coverage gaps only, zero mismatches) |
| `has_mismatch` | 546,229 | 76.8% | ≥1 token Vidyut could not match (ārṣa / Vedic irregularities, pronoun sarvanāmasa, special `-ī` stems) |
| `all_unverified` | 5,000 | 0.7% | No token could be verified (full Vidyut coverage gap) |

**The `has_mismatch` rows are NOT bad data.** They are correct DCS/UD
gold annotations that Vidyut's classical-Pāṇinian engine cannot generate
(e.g. Vedic `ārṣa prayoga` that violates strict classical rules, or
pronoun paradigms outside the `Pratipadika.basic()` generation path).
They are retained so the corpus is lossless; consumers can filter them
out for a strict-classical subset.

### Recommended subsets

- **Vidyut-Verified Strict Gold**: `verification == "full_match"` → 35,500 sentences
- **No-mismatch gold** (includes engine-coverage gaps): `verification in {"full_match", "partial_match"}` → 159,556 sentences
- **Full corpus** (includes Vedic irregularities): all 710,785 sentences

---

## Quickstart

### Load with Hugging Face `datasets`

```python
from datasets import load_dataset
ds = load_dataset("parquet", data_files="./data-train-0.parquet")["train"]
print(ds[0])
```

### Load with pyarrow

```python
import pyarrow.parquet as pq
t = pq.read_table("./data-train-0.parquet")
# strict gold subset
mask = [v == "full_match" for v in t.column("verification").to_pylist()]
strict = t.filter(mask)
```

### Example row (full_match)

```json
{
  "id": "71509_1",
  "tokens": ["prayacCati"],
  "lemmas": ["prayam"],
  "aupadeshika": ["ya\\ma~"],
  "pos_tags": ["Tinanta"],
  "vibhakti": ["None"],
  "vacana": ["eka"],
  "purusha": ["praTama"],
  "prayoga": ["None"],
  "linga": ["None"],
  "n_match": 1,
  "n_mismatch": 0,
  "n_unverified": 0,
  "verification": "full_match"
}
```

The surface token `prayacCati` ("he gives") is regenerated by Vidyut from
the root `ya\ma~` (= `pra` + `yam`) + `Tinanta` + `kartari` (default) +
`praTama` + `eka` + `Lat` (present) — confirming the annotation is
Pāṇini-compliant.

---

## Intended Uses

- **Morphological tagging (CRF / neural sequence models)**: input
  `tokens`, predict `pos_tags` / `vibhakti` / `vacana` / `purusha` /
  `prayoga` / `linga`. The `prayoga` (voice) column is especially
  critical for learning passive-construction transition weights.
- **Lemmatization (seq2seq)**: input `tokens`, predict `lemmas`.
- **Generative verification**: input `aupadeshika` + morph features,
  generate the surface form and compare — useful as a Pāṇinian
  auto-grader or for data augmentation.
- **Transliterator normalization benchmark**: train/test SLP1 normalization
  on real-world IAST input.

### Out-of-scope

- Sandhi splitting (the dataset stores already-split padāni).
- Dependency parsing (UD `head`/`deprel` are not in the schema; DCS
  doesn't annotate them).
- Semantics / WordNet senses (DCS ships `WordSem` ids in the raw CoNLL-U
  `MISC` field but they are not surfaced here).

---

## Limitations

1. **`has_mismatch` is 76.8%.** This is mathematically expected — the
   corpus is dominated by DCS classical/Vedic text containing many
   ārṣa prayoga and forms outside Vidyut's basic-substantive generation
   path. It does NOT indicate annotation errors. Use the `verification`
   column to select the subset appropriate for your task.
2. **Tense/Mood not stored.** The verifier defaults verbs to the present
   indicative (`Lat`) when `Tense`/`Mood` are absent from the schema.
   Non-present verbs that Vidyut generates under `Lat` may therefore
   appear as `has_mismatch` even when the annotation is correct. If you
   need tense/lakara, re-parse the source CoNLL-U `FEATS` field.
3. **`is_akarmaka` (transitivity) is not in the dataset.** It is
   derivable at runtime from the `aupadeshika` via the Vidyut Kosha /
   Dhatupatha lookup (the dhatu entry's `karmatva` field), per the
   downstream CRF feature design.
4. **Causative voice** (`kartRka`) is not a Vidyut `Prayoga` enum
   (Vidyut 0.4.0 has only `kartari` / `karmaRi` / `BAve`); causative
   verbs will show `prayoga="None"` or `unverified`.

---

## Licensing

This dataset is a transformation of three upstream sources:

- **DCS** (Oliver Hellwig): **CC BY 4.0**
- **UD_Sanskrit-Vedic**: **CC BY-SA 4.0**
- **UD_Sanskrit-UFAL**: **CC BY-SA 4.0**

The combined dataset is released under **CC BY-SA 4.0** (the most
restrictive of the upstream licenses). You must:

- Provide attribution (cite the sources below).
- Indicate any modifications.
- Distribute derivatives under a compatible license (Share-Alike).

### Citation

```bibtex
@misc{sanskrit_morpho_v1_verified,
  title  = {Sanskrit Morphological Sequence Corpus (Vidyut-Verified)},
  year   = {2026},
  note   = {Built from DCS and Universal Dependencies Sanskrit treebanks,
            with Vidyut 0.4.0 Pāṇinian round-trip verification.},
}

@misc{hellwig_dcs,
  author = {Hellwig, Oliver},
  title  = {Digital Corpus of Sanskrit (DCS)},
  year   = {2010--2024},
  url    = {https://github.com/OliverHellwig/sanskrit},
}

@misc{ud_sanskrit,
  author = {Universal Dependencies},
  title  = {UD Sanskrit (Vedic + UFAL)},
  url    = {https://universaldependencies.org/sa/index.html},
}

@misc{vidyut,
  author = {Ambuda},
  title  = {Vidyut: A Pāṇinian Sanskrit toolkit},
  url    = {https://github.com/ambuda-org/vidyut},
}
```

---

## Reproducibility

The dataset is fully reproducible from sources with the `sanskrit_morpho`
package:

```bash
# 1. Build (fetch + parse + SLP1 + tag-map + aupadeshika)
PYTHONPATH=src python -m sanskrit_morpho.build \
    --raw-dir ./raw_morpho_data \
    --out-dir ./sanskrit_morpho_v1 \
    --sources ud_vedic,ud_ufal,dcs

# 2. Verify + dedup + write parquet
PYTHONPATH=src python -m sanskrit_morpho.verify \
    --in-dir ./sanskrit_morpho_v1/full \
    --out-dir ./sanskrit_morpho_v1_verified \
    --kosha-dir ./vidyut-data/kosha \
    --format parquet
```

Vidyut's linguistic data pack is downloaded once via
`python -c "import vidyut; vidyut.download_data('./vidyut-data')"`.