README.md

---
configs:
- config_name: default
  data_files:
  - path: train-00000-of-00001.parquet
    split: train
  - path: val-00000-of-00001.parquet
    split: val
  - path: test-00000-of-00001.parquet
    split: test
dataset_info:
  config_name: default
  dataset_size: 10208409
  download_size: 10208409
  features:
  - dtype: string
    name: id
  - dtype: string
    name: author
  - dtype: string
    name: title
  - dtype: string
    name: topic
  - dtype: string
    name: text
  - dtype: string
    name: topic_code
  splits:
  - name: train
    num_bytes: 6060818
    num_examples: 8574
  - name: val
    num_bytes: 2150537
    num_examples: 2825
  - name: test
    num_bytes: 1997054
    num_examples: 2935
---

# English Poetry Topic Clustering Benchmark

## Dataset Description

English poetry topic clustering benchmark for evaluating embedding models' performance on unsupervised poetry topic clustering tasks. Each record contains a poem and its topic clustering label.

This dataset is derived from [AJMC2002/poems](https://huggingface.co/datasets/AJMC2002/poems) and split into train/validation/test sets with a 6:2:2 ratio while maintaining topic distribution.

## Dataset Statistics

| Metric | Value |
|--------|-------|
| **Total Poems** | 14,334 poems |
| **Train Set** | 8,574 poems |
| **Validation Set** | 2,825 poems |
| **Test Set** | 2,935 poems |
| **Total File Size** | 9.7 MB |

## Data Fields

- `id`: Unique poem identifier
- `author`: Poem author
- `title`: Poem title
- `text`: Poem body/content
- `topic`: Original topic label
- `topic_code`: Topic clustering label (used for clustering evaluation)

## Dataset Splits

The dataset is split into three sets:
- **train**: Training set (60% of data)
- **val**: Validation set (20% of data)  
- **test**: Test set (20% of data)

The split maintains topic distribution across all sets.

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("PoetryMTEB/EnglishPoetryTopicClustering")

# Access different splits
train_data = dataset['train']
val_data = dataset['val']
test_data = dataset['test']
```

## Citation

If you use this dataset, please cite the original source:
- Original dataset: AJMC2002/poems