README.md

metadata

license: unknown
task_categories:
  - other
language:
  - en
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
pretty_name: English Poetry Topic Clustering
tags:
  - poetry
  - english
  - topic-clustering
  - clustering
  - mteb
  - poetrymteb
  - embedding-evaluation
annotations_creators:
  - found
source_datasets:
  - AJMC2002/poems
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*
    default: true
dataset_info:
  - config_name: default
    features:
      - name: id
        dtype: string
      - name: author
        dtype: string
      - name: title
        dtype: string
      - name: poem
        dtype: string
      - name: label
        dtype: int64
      - name: label_name
        dtype: string
      - name: label_name_en
        dtype: string
      - name: label_name_zh
        dtype: string
      - name: language
        dtype: string
    splits:
      - name: test
        num_examples: 14334

English Poetry Topic Clustering

Clustering benchmark of English poetry by topic keywords for PoetryMTEB / MTEB-style embedding evaluation.

Derived from AJMC2002/poems (topic-tagged poems originally parsed from a Kaggle poetry collection).

Dataset Card

Item	Description
Dataset version (PoetryMTEB)	1.0.0
Task	Clustering (unsupervised embedding evaluation)
Languages	English (en)
Unit	Full poem text (poem)
Labels	144 topic keywords (label_name = English; label_name_zh = Chinese)
Size	test=14334 (no train/validation — not required for MTEB Clustering)
Evaluation metrics	Typically V-measure (K-Means on embeddings)

Prior Hub packaging exposed train/val/test (~6:2:2). This release merges all poems into test for standard MTEB Clustering probing.

Label taxonomy (144 topics)

Preview (top by frequency; full list in label_taxonomy.json):

id	label_name (en)	name_zh	count
0	alone	孤独	100
1	america	美国	100
2	angel	天使	100
3	animal	动物	100
4	baby	婴儿	100
5	beach	海滩	100
6	beautiful	美丽	100
7	beauty	美	100
8	birth	出生	100
9	brother	兄弟	100
10	butterfly	蝴蝶	100
11	children	儿童	100
12	christmas	圣诞节	100
13	cinderella	灰姑娘	100
14	crazy	疯狂	100
15	dance	舞蹈	100
16	dark	黑暗	100
17	death	死亡	100
18	destiny	命运	100
19	dream	梦想	100
…	(all 144 topics)	…	…

Features

Field	Type	Description
id	string	Upstream poem id
author	string	Poet name
title	string	Poem title
poem	string	Clustering input (formerly text)
label	int64	Topic class id (0 … 143)
label_name	string	Topic keyword (formerly topic)
label_name_en	string	Same as label_name (English)
label_name_zh	string	Chinese gloss of the topic keyword
language	string	Always en

Construction method

1. Load prior PoetryMTEB/EnglishPoetryTopicClustering train / val / test.
2. Merge all rows into a single test split.
3. Map topics → integer label + Chinese glosses; rename text→poem.
4. Write parquet, Dataset Card, taxonomy, checksums, eval script.

Supporting materials

Path	Content
VERSION / LICENSE / CITATION.* / metadata.json	Packaging metadata
label_taxonomy.json	Full EN/ZH topic codebook
checksums.sha256	File checksums
scripts/evaluate_english_topic_clustering.py	Baseline V-measure probe

How to load

from datasets import load_dataset

ds = load_dataset("PoetryMTEB/EnglishPoetryTopicClustering")
ex = ds["test"][0]
print(ex["poem"][:200])
print(ex["label_name"], ex["label_name_zh"], ex["label"])

Citation / provenance

• Upstream: AJMC2002/poems
• This packaging: PoetryMTEB/EnglishPoetryTopicClustering (version 1.0.0)

License

Upstream license is not clearly stated (AJMC2002/poems / Kaggle provenance). Marked unknown; use for research and respect original poem copyrights.

---

英语诗歌主题聚类（中文说明）

MTEB 聚类评测集：仅 test=14334，144 个英文话题关键词，并提供中文译名 label_name_zh。