Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
multi-class-classification
Languages:
Bengali
Size:
1K - 10K
License:
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
- Load
SAHITTO.ods(sheetPoems:title,poem,writer,label). - Keep all 11 category labels; map to contiguous
labelids (frequency-descending). - 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}
}
- Mendeley: https://data.mendeley.com/datasets/zgmrk5m566/2
- Paper: https://doi.org/10.1109/AIIoT58121.2023.10174592
- This Hub packaging:
PoetryMTEB/SahittoCategoryClassification