README.md

---
license: agpl-3.0
task_categories:
- text-classification
task_ids:
- multi-label-classification
language:
- en
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
pretty_name: PoetryFoundationSubjectsClassification
tags:
- poetry
- multi-label-classification
- theme-classification
- subjects
- mteb
- poetrymteb
- embedding-evaluation
annotations_creators:
- derived
source_datasets:
- suayptalha/Poetry-Foundation-Poems
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: labels_l1
    sequence: string
  - name: labels_l2
    sequence: string
  - name: labels_l1_names
    sequence: string
  - name: labels_l2_names
    sequence: string
  splits:
  - name: train
    num_examples: 10259
  - name: validation
    num_examples: 1283
  - name: test
    num_examples: 1283
---

# PoetryFoundationSubjectsClassification

Multi-label **poetry subject / theme classification** dataset for PoetryMTEB embedding evaluation, derived from Poetry Foundation poem subjects.

## Dataset Card

| Item | Description |
|------|-------------|
| **Source** | [suayptalha/Poetry-Foundation-Poems](https://huggingface.co/datasets/suayptalha/Poetry-Foundation-Poems); subject taxonomy aligned with [Poetry Foundation Topics](https://www.poetryfoundation.org/topics) |
| **Languages** | English (`en`) |
| **Size** | train=10259; validation=1283; test=1283 (poems with ≥1 mapped subject) |
| **Label type** | Multi-label subjects at two granularities: L1 (coarse) and L2 (fine), with hierarchical codes |
| **Splits** | Poet-aware ~8:1:1 split (`train` / `validation` / `test`) |
| **Construction** | Parse original `Tags` → exclude occasions/holidays & emotion-like tags → alias to official subject names → assign `S{i}` / `S{i}-{j}` codes |
| **License** | [GNU Affero General Public License v3.0](https://www.gnu.org/licenses/agpl-3.0.html) (same family as the upstream HF dataset) |
| **Evaluation metrics** | Multi-label classification on embeddings: **macro/micro F1**, **Average Precision (AP)**; evaluate **L1 and L2 separately** |

---

## Features

| Field | Type | Description |
|-------|------|-------------|
| `id` | string | Example id |
| `title` | string | Poem title |
| `author` | string | Poet name |
| `poem` | string | Poem body |
| `labels_l1` | list[string] | Coarse subject codes, e.g. `S1`, `S6` |
| `labels_l2` | list[string] | Fine subject codes with parent index, e.g. `S1-8`, `S6-6` |
| `labels_l1_names` | list[string] | Human-readable L1 names |
| `labels_l2_names` | list[string] | Human-readable paths `Parent/Child` |

Full codebook: `label_taxonomy.json`.

### L1 codes

| Code | Subject |
|------|---------|
| `S1` | Love |
| `S2` | Nature |
| `S3` | Social Commentaries |
| `S4` | History & Politics |
| `S5` | Religion |
| `S6` | Living |
| `S7` | Time & Brevity |
| `S8` | Relationships |
| `S9` | Activities |
| `S10` | Arts & Sciences |
| `S11` | Mythology & Folklore |

L2 codes are `S{i}-{j}` where `j` is the child index under that L1 (see taxonomy file). Example: `Living/Health & Illness` → `S6-6` (index depends on Living child order).

---

## Construction method

1. **Upstream text & tags** from [suayptalha/Poetry-Foundation-Poems](https://huggingface.co/datasets/suayptalha/Poetry-Foundation-Poems) (`Title`, `Poet`, `Poem`, `Tags`).
2. **Taxonomy** follows Poetry Foundation [Topics / Subjects](https://www.poetryfoundation.org/topics), with project adjustments:
   - Occasions/holidays excluded
   - Emotion-like `The Mind` / `Horror` excluded from subject labels
   - `Health & Illness` placed under **Living**
   - Dataset aliases (e.g. `Death`→`Death & Dying`, `First Love`→`New Love`)
   - `Classic Love` kept as a Love L2 leaf
3. **Hierarchical codes**: L1=`S{i}`; L2=`S{i}-{j}` (parent index explicit in the L2 code).
4. **Split**: group by author, then allocate poet groups to train/validation/test ≈ 80%/10%/10%.

---

## Label statistics (all splits pooled)

| Level | #label types | notes |
|-------|-------------:|-------|
| L1 | 11 | codes in use |
| L2 | 71 | codes in use |
| Avg L1 / poem | 2.55 | |
| Avg L2 / poem | 2.27 | |

---

## How to load

```python
from datasets import load_dataset

ds = load_dataset("PoetryMTEB/PoetryFoundationSubjectsClassification")
print(ds["test"][0]["title"], ds["test"][0]["labels_l1"], ds["test"][0]["labels_l2_names"])
```

For embedding evaluation, encode `poem` (optionally prepend `title`), then train multi-label classifiers separately on `labels_l1` and `labels_l2`.

---

## License

Distributed under **GNU Affero General Public License v3.0 (AGPL-3.0)**, consistent with the upstream dataset license on Hugging Face.

---

## Citation / provenance

Please credit the upstream corpus and Poetry Foundation subject browsing structure:

- Dataset: [https://huggingface.co/datasets/suayptalha/Poetry-Foundation-Poems](https://huggingface.co/datasets/suayptalha/Poetry-Foundation-Poems)
- Subjects / topics: [https://www.poetryfoundation.org/topics](https://www.poetryfoundation.org/topics)

This Hub packaging: `PoetryMTEB/PoetryFoundationSubjectsClassification`.