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metadata
license: mit
task_categories:
  - text-classification
task_ids:
  - multi-class-classification
language:
  - te
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
pretty_name: ChandassuMeterClassification
tags:
  - poetry
  - telugu
  - meter-classification
  - chandassu
  - text-classification
  - mteb
  - poetrymteb
  - embedding-evaluation
annotations_creators:
  - expert-generated
source_datasets:
  - BodduSriPavan111/chandassu
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
    default: true
dataset_info:
  - config_name: default
    features:
      - name: id
        dtype: string
      - name: poem
        dtype: string
      - name: label
        dtype: int64
      - name: label_name
        dtype: string
    splits:
      - name: train
        num_examples: 3721
      - name: test
        num_examples: 930

ChandassuMeterClassification

Single-label Telugu poetic meter (chandassu / type) classification for PoetryMTEB, derived from the Chandassu corpus (Kaggle / Mendeley / Hugging Face; local Chandassu_Dataset.csv).

Dataset Card

Item Description
Source Kaggle; Mendeley Data (DOI: 10.17632/46wrt7v53c.1); Hugging Face; paper arXiv:2510.01233
Languages Telugu (te)
Size train=3721; test=930 (no validation)
Classes 8 meters (type field)
Splits Stratified ≈ 80% / 19% by meter, with cross-dataset anti-leakage vs PoetryMTEB/Padyam2GadyamMeterClassification
License MIT (same as upstream)
Evaluation metrics Classification on embeddings: accuracy, macro/weighted F1

Cross-dataset split constraint

Relative to PoetryMTEB/Padyam2GadyamMeterClassification:

  1. Chandassu train does not contain poems that appear in Padyam test
  2. Chandassu test does not contain poems that appear in Padyam train

Matching uses whitespace-normalized poem text. Forced assignments:
Padyam-train overlaps → Chandassu train (20 poems);
Padyam-test overlaps → Chandassu test (6 poems).

Verified after split: chandassu_train ∩ pady_test = 0, chandassu_test ∩ pady_train = 0.


Features

Field Type Description
id string Example id
poem string Telugu poem (raw_padyam_text)
label int64 Meter class index
label_name string Meter type (romanized), e.g. kandamu

Codebook: label_taxonomy.json.

Labels

id label_name gloss count
0 aataveladi Āṭaveladi / ఆటవెలది (戏舞体) 995
1 kandamu Kandam / కందం (坎达体) 683
2 teytageethi Tēṭagīti / తేటగీతి (澄歌体) 676
3 seesamu Sīsamu / సీసము (席萨体) 672
4 mattebhamu Mattebhamu / మత్తేభము (醉象律) 617
5 champakamaala Champakamāla / చంపకమాల (瞻波迦鬘) 389
6 vutpalamaala Utpalamāla / ఉత్పలమాల (优钵罗鬘) 329
7 saardulamu Śārdūlamu / శార్దూలము (虎律) 290

Construction method

  1. Load local Chandassu_Dataset.csv / upstream Chandassu.
  2. Load PoetryMTEB/Padyam2GadyamMeterClassification train/test poems; force overlapping Chandassu rows into compatible splits (anti-leakage).
  3. Stratified train/test split of remaining rows by type (seed=42).
  4. Map type → contiguous label ids (frequency-descending).

How to load

from datasets import load_dataset

ds = load_dataset("PoetryMTEB/ChandassuMeterClassification")
print(ds["test"][0]["label_name"], ds["test"][0]["poem"][:60])

License

Distributed under the MIT License, consistent with the upstream Chandassu release.


Citation / provenance

@misc{pavan2025computationalsociallinguisticstelugu,
      title={Computational Social Linguistics for Telugu Cultural Preservation: Novel Algorithms for Chandassu Metrical Pattern Recognition}, 
      author={Boddu Sri Pavan and Boddu Swathi Sree},
      year={2025},
      eprint={2510.01233},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2510.01233}, 
}