README.md

metadata

license: other
task_categories:
  - text-classification
task_ids:
  - multi-class-classification
language:
  - ta
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
pretty_name: TamilSangamLiteraryDeviceClassification
tags:
  - poetry
  - tamil
  - sangam
  - literary-device
  - rhetoric
  - multi-class-classification
  - mteb
  - poetrymteb
  - embedding-evaluation
annotations_creators:
  - expert-generated
source_datasets:
  - Tamil-Sangam-Literature-Dataset
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: line_number
        dtype: int64
      - name: poem
        dtype: string
      - name: transliteration
        dtype: string
      - name: english
        dtype: string
      - name: label
        dtype: int64
      - name: label_name
        dtype: string
      - name: original_device
        dtype: string
    splits:
      - name: train
        num_examples: 840
      - name: test
        num_examples: 211

TamilSangamLiteraryDeviceClassification

Multi-class literary device / rhetoric classification for classical Tamil Sangam verse lines (PoetryMTEB), derived from the Tamil Sangam Literature Dataset.

Upstream Literary Devices strings are noisy (44 free-text labels, heavily skewed to Imagery). We merge near-duplicates and rare labels into a closed 8-class taxonomy, then create a stratified train/test split for embedding evaluation.

Dataset Card

Item	Description
Source	Tamil Sangam Literature Dataset (Tamil Dangam.csv)
Languages	Tamil (ta); English translation & Latin transliteration kept as auxiliaries
Unit	Verse line (poem = Original Tamil Text)
Labels	8 merged literary-device classes
Size	train=840; test=211 (dropped empty text/label: 1)
Splits	Stratified by merged label ≈ 80% / 20%, seed=42
License	Upstream repository terms (no explicit SPDX; research use — attribute source)
Evaluation metrics	Classification on embeddings: accuracy, macro/micro F1

Label taxonomy (8)

id	label_name	zh	gloss	train	test	total
0	Imagery	意象描写	Imagery and related visual/emotional descriptive imagery	486	122	608
1	Simile	明喻	Simile	102	26	128
2	Descriptive	描述	Descriptive / setting description	91	23	114
3	Narrative	叙事	Narrative / narrative reflection	41	10	51
4	DirectAddress	直接称呼	Direct address to a hearer / addressee	36	9	45
5	Metaphor	隐喻	Metaphor	26	6	32
6	RhetoricalQuestion	反问/设问	Rhetorical question / question	11	3	14
7	Other	其他修辞	Merged rare devices (symbolism, personification, hyperbole, etc.)	47	12	59

Codebook: label_taxonomy.json.

Merge rules (upstream → merged)

original_device	merged label
Description	Descriptive
Descriptive	Descriptive
Setting	Descriptive
Direct Address	DirectAddress
Direct address	DirectAddress
Descriptive imagery	Imagery
Emotional imagery	Imagery
Imagery	Imagery
Visual imagery	Imagery
Metaphor	Metaphor
Narrative	Narrative
Narrative Reflection	Narrative
Admonition	Other
Conditional	Other
Conditional Statement	Other
Conditional statement	Other
Contrast	Other
Dialogue	Other
Direct Speech	Other
Direct speech	Other
Direct statement	Other
Emotional Expression	Other
Encouragement	Other
Exclamation	Other
Expression of Suffering	Other
Expression of longing	Other
Expression of well-wishes	Other
Historical Reference	Other
Hyperbole	Other
Hypothetical	Other
Irony	Other
Negative statement	Other
Personification	Other
Philosophical Reflection	Other
Quotation	Other
Reference	Other
Repetition	Other
Symbolism	Other
Theme	Other
Wish	Other
Question	RhetoricalQuestion
Rhetorical Question	RhetoricalQuestion
Rhetorical question	RhetoricalQuestion
Simile	Simile

Any upstream string not listed above is mapped to Other.

Rationale: Imagery alone covers ~57% of rows; many remaining labels have ≤4 examples and cannot support a reliable test split. Merging yields a compact, imbalanced-but-usable 8-way task.

Features

Field	Type	Description
id	string	Example id (sangam-device-{line_number})
line_number	int64	Upstream Line Number
poem	string	Classification input: Original Tamil Text
transliteration	string	Latin-script transliteration (auxiliary)
english	string	English translation (auxiliary)
label	int64	Merged class id (0–7)
label_name	string	Canonical English class name
original_device	string	Raw upstream Literary Devices string (before merge)

Construction method

1. Load Tamil Dangam.csv (1052 lines).
2. Map Literary Devices → 8-way taxonomy via the merge table above.
3. Drop rows with empty Tamil text or missing device label.
4. Stratified train/test split by label_name (seed=42, ≈ 80/19).

How to load

from datasets import load_dataset

ds = load_dataset("PoetryMTEB/TamilSangamLiteraryDeviceClassification")
print(ds["train"][0]["poem"])
print(ds["train"][0]["label_name"], ds["train"][0]["original_device"])

Intended use / limitations

• PoetryMTEB multi-class probing of Tamil poetic line embeddings for rhetorical style.
• Labels inherit upstream annotation quality; Imagery remains dominant after merge.
• Other is a residual bucket — do not over-interpret fine-grained rhetoric inside it.
• Companion bitext release: PoetryMTEB/TamilSangamBitextMining.

Citation / provenance

Please attribute the upstream dataset:

• https://github.com/kameshkanna/Tamil-Sangam-Literature-Dataset
• This Hub packaging: PoetryMTEB/TamilSangamLiteraryDeviceClassification

License

Follow the upstream GitHub repository terms. This packaging redistributes line texts and merged device labels for research evaluation under PoetryMTEB.