README.md

---
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)](https://data.mendeley.com/datasets/zgmrk5m566/2), DOI [10.17632/zgmrk5m566.2](https://doi.org/10.17632/zgmrk5m566.2); poems collected from [banglarkobita.com](https://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](https://creativecommons.org/licenses/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

```python
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:

```bibtex
@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:

```bibtex
@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](https://data.mendeley.com/datasets/zgmrk5m566/2)
- Paper: [https://doi.org/10.1109/AIIoT58121.2023.10174592](https://doi.org/10.1109/AIIoT58121.2023.10174592)
- This Hub packaging: `PoetryMTEB/SahittoCategoryClassification`