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Add split statistics and per-category counts by train/val/test
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metadata
license: cc-by-4.0
task_categories:
  - text-classification
task_ids:
  - multi-class-classification
language:
  - bn
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
pretty_name: SahittoCategoryClassification
tags:
  - poetry
  - bangla
  - bengali
  - genre-classification
  - theme-classification
  - text-classification
  - mteb
  - poetrymteb
  - embedding-evaluation
annotations_creators:
  - expert-generated
source_datasets:
  - bangla-poem-dataset-sahitto
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
    default: true
dataset_info:
  - config_name: default
    features:
      - name: id
        dtype: string
      - name: title
        dtype: string
      - name: author
        dtype: string
      - name: poem
        dtype: string
      - name: label
        dtype: int64
      - name: label_name
        dtype: string
    splits:
      - name: train
        num_examples: 1763
      - name: validation
        num_examples: 222
      - name: test
        num_examples: 222

SahittoCategoryClassification

Single-label Bangla poem category / genre classification for PoetryMTEB embedding evaluation, derived from the Bangla Poem Dataset (SAHITTO).

Dataset Card

Item Description
Source Bangla Poem Dataset (Mendeley), DOI 10.17632/zgmrk5m566.2; poems collected from banglarkobita.com
Languages Bangla / Bengali (bn)
Size 2207 poems total; train=1763 (79.9%); validation=222 (10.1%); test=222 (10.1%)
Classes 11 poem categories (题材/主题)
Splits Stratified by category ≈ 80% / 10% / 10% (train / validation / test), seed=42
License CC BY 4.0 (same as upstream)
Evaluation metrics Classification on embeddings: accuracy, macro/weighted F1

Split statistics

Split #poems ratio
train 1763 79.88%
validation 222 10.06%
test 222 10.06%
total 2207 100%

Per-category counts by split

id label_name Bangla train validation test total
0 Miscellaneous বিবিধ কবিতা 649 81 81 811
1 Love প্রেমের কবিতা 400 50 50 500
2 Metaphor রূপক কবিতা 252 32 32 316
3 Separation বিরহের কবিতা 105 13 13 131
4 Humanity মানবতাবাদী কবিতা 63 8 8 79
5 Children ছোটদের ছড়া-কবিতা 62 8 8 78
6 Patriotic দেশাত্মবোধক কবিতা 60 8 8 76
7 Policy নীতি কবিতা 53 7 7 67
8 Religious ধর্মীয় কবিতা 50 6 6 62
9 Nature প্রকৃতির কবিতা 43 6 6 55
10 War যুদ্ধের কবিতা 26 3 3 32
Sum 1763 222 222 2207

Codebook: label_taxonomy.json.


Features

Field Type Description
id string Example id
title string Poem title
author string Writer name (Bangla)
poem string Poem body
label int64 Category index (0 … C−1)
label_name string Category name in English (upstream label)

Construction method

  1. Load SAHITTO.ods (sheet Poems: title, poem, writer, label).
  2. Keep all 11 category labels; map to contiguous label ids (frequency-descending).
  3. Stratified train / validation / test split by category (each class appears in all three splits).

How to load

from datasets import load_dataset

ds = load_dataset("PoetryMTEB/SahittoCategoryClassification")
print(ds["test"][0]["label_name"], ds["test"][0]["title"])

For embedding evaluation, encode poem (optionally prepend title), fit a classifier on train, optionally tune on validation, score on test.


License

Distributed under Creative Commons Attribution 4.0 International (CC BY 4.0), consistent with the upstream Mendeley release.


Citation / provenance

Please cite the Bangla poem genre classification paper and the Mendeley dataset:

@INPROCEEDINGS{10174592,
  author={Pasha, Syed Tangim and Islam, Ashraful and Rahman, Mohammed Masudur and Ahmed, Eshtiak and Ahmed Foysal, Md. Ferdouse and Zahangir Alam, Md},
  booktitle={2023 IEEE World AI IoT Congress (AIIoT)}, 
  title={Genre Classification of Bangla Poem Using Machine Learning and Deep Learning Techniques}, 
  year={2023},
  pages={0236-0242},
  doi={10.1109/AIIoT58121.2023.10174592}
}

Dataset citation:

@misc{pasha2022banglapoem,
  author = {Pasha, Syed Tangim},
  title = {Bangla Poem Dataset},
  year = {2022},
  publisher = {Mendeley Data},
  version = {V2},
  doi = {10.17632/zgmrk5m566.2},
  url = {https://data.mendeley.com/datasets/zgmrk5m566/2}
}