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+---
+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-0.parquet
+---
+
+# 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')"`.