README.md

---
license: cc-by-nc-4.0
task_categories:
- text-classification
- sentence-similarity
task_ids:
- semantic-similarity-classification
language:
- zh
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
pretty_name: CCPM (Pair Classification)
tags:
- poetry
- pair-classification
- classical-chinese
- chinese-poetry
- retrieval
- mteb
- poetrymteb
- semantic-similarity
annotations_creators:
- derived
source_datasets:
- original
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: sentence1
    dtype: string
  - name: sentence2
    dtype: string
  - name: labels
    dtype: int64
  splits:
  - name: train
    num_bytes: 4774437
    num_examples: 87112
  - name: validation
    num_bytes: 596643
    num_examples: 10880
  - name: test
    num_bytes: 596741
    num_examples: 10880
  download_size: 5967821
  dataset_size: 5967821
---

# CCPM — Pair Classification (PoetryMTEB)

Chinese Classical Poetry Matching Dataset (**CCPM**), reformatted as an **MTEB-style Pair Classification** task for PoetryMTEB embedding evaluation.

Upstream source: [CCPM](https://arxiv.org/abs/2106.01979) (Li et al., 2021). Original task: given a modern Chinese description, select the matching classical poetry line from four candidates.

---

## Dataset Card

| Item | Description |
|------|-------------|
| **Source** | CCPM — Chinese Classical Poetry Matching Dataset ([Li et al., 2021](https://arxiv.org/abs/2106.01979)) |
| **Languages** | Chinese (`zh`): modern Chinese description ↔ classical poetry line |
| **Size** | 108,872 labeled pairs (train 87,112; validation 10,880; test 10,880). Derived from 21,778 / 2,720 / 2,720 original MCQ instances (×4 choices) |
| **Label type** | Binary pair label: `labels=1` (semantic match), `labels=0` (hard negative) |
| **Splits** | `train` ← original train; `validation` / `test` ← original validation (see note below) |
| **Construction** | Expand each 4-way MCQ into 4 pairs `(sentence1=translation, sentence2=choice)` |
| **License** | [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) (PoetryMTEB redistribution; please cite original authors) |
| **Evaluation metrics** | MTEB Pair Classification: **Average Precision (AP)**, accuracy / F1 at best similarity threshold (cosine / Euclidean / Manhattan). Original CCPM MCQ metric: **Accuracy** |

> **Split note:** The public `test_public.jsonl` in the original release has **no gold answers**. For Pair Classification we use the original **validation** set as both `validation` and `test` (identical labeled pairs) so MTEB-style evaluators that read `test` can run. Do not treat train→test leakage across these two identical eval splits.

---

## Features

| Field | Type | Description |
|-------|------|-------------|
| `id` | `string` | Unique pair id |
| `sentence1` | `string` | Modern Chinese description (`translation`) |
| `sentence2` | `string` | Classical poetry line candidate (`choice`) |
| `labels` | `int64` | `1` = matching pair; `0` = non-matching (hard negative) |

Schema aligns with MTEB Pair Classification (`sentence1`, `sentence2`, `labels`).

---

## Construction method

1. **Upstream CCPM** builds each instance from classical–modern parallel poetry: the gold line is the correct choice; three distractors are retrieved as similar lines from a classical poetry corpus (hard negatives).
2. **This release** converts each MCQ instance into four binary pairs:

```text
for choice_i in choices:
    sentence1 = translation
    sentence2 = choice_i
    labels    = 1 if i == answer else 0
```

Class balance per split: **1 positive : 3 negatives**.

---

## Size by split

| Split | #pairs | #positive (`labels=1`) | #negative (`labels=0`) | Origin |
|-------|-------:|-----------------------:|-----------------------:|--------|
| train | 87,112 | 21,778 | 65,334 | original train (21,778×4) |
| validation | 10,880 | 2,720 | 8,160 | original valid (2,720×4) |
| test | 10,880 | 2,720 | 8,160 | same as validation (see note) |

---

## Evaluation metrics

**PoetryMTEB / MTEB Pair Classification**

| Metric | Role |
|--------|------|
| Average Precision (AP) | Ranking quality of pair similarity scores |
| Accuracy / F1 | Binary decision at best threshold over cosine / distance scores |

**Original CCPM (4-way matching)** used Accuracy over candidate indices.

---

## How to load

```python
from datasets import load_dataset

ds = load_dataset("PoetryMTEB/CCPM")
print(ds)
print(ds["test"][0])
# {'id': '...', 'sentence1': '...', 'sentence2': '...', 'labels': 0 or 1}
```

---

## License

Distributed under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).  
Please cite the original CCPM paper when using this data.

---

## Citation

```bibtex
@article{li2021CCPM,
  title = {CCPM: A Chinese Classical Poetry Matching Dataset},
  author = {Li, Wenhao and Qi, Fanchao and Sun, Maosong and Yi, Xiaoyuan and Zhang, Jiarui},
  journal = {arXiv preprint arXiv:2106.01979},
  year = {2021},
  url = {https://arxiv.org/abs/2106.01979}
}
```

This Hub dataset: `PoetryMTEB/CCPM` (Pair Classification packaging for PoetryMTEB).