Rebuild from Dataverse Excel: correct 0-6 labels + ZH/EN names + Dataset Card (train/test only)

CITATION.cff

@@ -1,21 +1,10 @@
 cff-version: 1.2.0
 message: Please cite the JOCH 2024 paper and the Dataverse dataset.
 title: "Classical Chinese Poetry Theme Classification (PoetryMTEB)"
-version: "1.0.0"
+version: "1.0.1"
 license: CC0-1.0
 authors:
   - family-names: Hou
     given-names: Jingrui
   - family-names: Zhang
     given-names: Shitou
-preferred-citation:
-  type: article
-  title: "Exploring Thematic Diversity in Classical Chinese Poetry: A Novel Dataset and a BERT-enhanced Ensemble Learning Approach"
-  authors:
-    - family-names: Hou
-      given-names: Jingrui
-    - family-names: Zhang
-      given-names: Shitou
-  year: 2024
-  journal: Journal on Computing and Cultural Heritage
-  doi: "10.1145/3685679"

LICENSE

@@ -1,11 +1,6 @@
 Creative Commons CC0 1.0 Universal
-=================================
-
-This PoetryMTEB packaging redistributes the Classical Chinese poetry thematic
-classification dataset under CC0 1.0, matching the Harvard Dataverse release:
-
-  https://doi.org/10.7910/DVN/6QJ7RK
 
+Upstream: https://doi.org/10.7910/DVN/6QJ7RK
 Legal code: https://creativecommons.org/publicdomain/zero/1.0/legalcode
 
-Please cite Hou & Zhang (JOCH 2024) and the Dataverse deposit when using the data.
+Please cite Hou & Zhang (JOCH 2024) and the Dataverse deposit.

README.md

@@ -58,34 +58,34 @@ dataset_info:
 
 Multi-class **theme classification** of **classical Chinese poetry** for PoetryMTEB embedding evaluation.
 
-Upstream dataset: [A thematic classification dataset for Classical Chinese poetry](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/6QJ7RK) (Hou, Harvard Dataverse, [DOI](https://doi.org/10.7910/DVN/6QJ7RK)), accompanying Hou & Zhang, *JOCH* 2024 ([paper](https://doi.org/10.1145/3685679)). Nearly 3,000 annotated poems across **seven** themes from the Chunqiu period through the Qing dynasty.
+Upstream: [A thematic classification dataset for Classical Chinese poetry](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/6QJ7RK) (Hou, Harvard Dataverse, [DOI](https://doi.org/10.7910/DVN/6QJ7RK)), with Hou & Zhang, *JOCH* 2024 ([paper](https://doi.org/10.1145/3685679)). Nearly 3,000 annotated poems across **seven** themes (Chunqiu → Qing).
 
 ## Dataset Card
 
 | Item | Description |
 |------|-------------|
-| **Dataset version (PoetryMTEB)** | `1.0.0` |
-| **Source** | [Harvard Dataverse DVN/6QJ7RK](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/6QJ7RK) |
+| **Dataset version (PoetryMTEB)** | `1.0.1` |
+| **Source** | [Harvard Dataverse DVN/6QJ7RK](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/6QJ7RK) (`train.xlsx` / `test.xlsx`) |
 | **Paper** | Hou J, Zhang S. *Exploring Thematic Diversity in Classical Chinese Poetry…* *JOCH* 17(4), 2024 ([DOI](https://doi.org/10.1145/3685679)) |
 | **Languages** | Classical Chinese / Chinese (`zh`) |
 | **Unit** | Full poem text (`poem`) |
-| **Labels** | Single-label **7** themes (`label` 0–6) |
+| **Labels** | Single-label **7** themes (`label` **0–6**, matching Dataverse README) |
 | **Size** | train=2624; test=292; **total=2916** |
-| **Splits** | **train / test only** (no validation), preserved from prior PoetryMTEB / upstream Excel split |
+| **Splits** | **train / test only** (no validation) |
 | **License** | [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) |
-| **Evaluation metrics** | Accuracy, macro/micro F1 (embedding classification probe) |
+| **Evaluation metrics** | Accuracy, macro/micro F1 |
 
 ## Label taxonomy (7)
 
 | id | label_name (zh) | name_en | train | test | total |
 |---:|-----------------|---------|------:|-----:|------:|
-| 0 | `爱情婚姻` | Love and Marriage | 0 | 0 | 0 |
-| 1 | `交友送别` | Friendship and Farewell | 269 | 43 | 312 |
-| 2 | `羁旅思乡` | Journey and Homesickness | 621 | 74 | 695 |
-| 3 | `边塞战争` | Frontier and War | 318 | 35 | 353 |
-| 4 | `山水田园` | Landscape and Countryside | 207 | 23 | 230 |
-| 5 | `咏史怀古` | History and Nostalgia | 337 | 31 | 368 |
-| 6 | `托物言志` | Expressing emotions through objects | 872 | 86 | 958 |
+| 0 | `爱情婚姻` | Love and Marriage | 269 | 43 | 312 |
+| 1 | `交友送别` | Friendship and Farewell | 621 | 74 | 695 |
+| 2 | `羁旅思乡` | Journey and Homesickness | 318 | 35 | 353 |
+| 3 | `边塞战争` | Frontier and War | 207 | 23 | 230 |
+| 4 | `山水田园` | Landscape and Countryside | 337 | 31 | 368 |
+| 5 | `咏史怀古` | History and Nostalgia | 354 | 33 | 387 |
+| 6 | `托物言志` | Expressing emotions through objects | 518 | 53 | 571 |
 
 | id | Description (EN) |
 |---:|------------------|
@@ -97,26 +97,24 @@ Upstream dataset: [A thematic classification dataset for Classical Chinese poetr
 | 5 | Historical events, figures, and cultural memory; nostalgia. |
 | 6 | Thoughts and feelings conveyed via portrayal/symbolism of objects. |
 
-Codebook: `label_taxonomy.json`.  
-**Note:** Upstream Excel uses codes **0–6**. An earlier Hub release used **1–7**; this packaging remaps to **0–6** to match the Dataverse README.
+Codebook: `label_taxonomy.json`.
 
 ## Features
 
 | Field | Type | Description |
 |-------|------|-------------|
-| `id` | string | Example id (`ccptc-{split}-…`) |
-| `poem` | string | **Classification input** (formerly `text`) |
+| `id` | string | Example id (`ccptc-{split}-{row}`) |
+| `poem` | string | **Classification input** |
 | `label` | int64 | Theme id **0–6** |
 | `label_name` | string | Chinese theme name |
 | `label_name_en` | string | English theme name |
-| `label_name_zh` | string | Chinese theme name (same as `label_name`) |
+| `label_name_zh` | string | Chinese theme name |
 
 ## Construction method
 
-1. Load existing `PoetryMTEB/ClassicalChinesePoetryThemeClassification` train/test parquet.
-2. Remap `label` 1–7 → 0–6; attach bilingual names.
-3. Rename `text`→`poem`; assign stable ids.
-4. Write parquet under `data/`, Dataset Card, `dataset_infos.yaml`, taxonomy, checksums, eval script.
+1. Download upstream `train.xlsx` / `test.xlsx` from Dataverse.
+2. Keep official **0–6** labels; attach ZH/EN names from the Dataverse README.
+3. Write PoetryMTEB parquet + Dataset Card + supporting materials.
 
 ## Supporting materials
 
@@ -138,10 +136,6 @@ print(ds["train"][0]["poem"][:80])
 print(ds["train"][0]["label_name"], ds["train"][0]["label_name_en"], ds["train"][0]["label"])
 ```
 
-## Intended use
-
-PoetryMTEB / MTEB-style **multi-class classification** probing of classical Chinese poem embeddings (theme recognition).
-
 ## Citation
 
 ```bibtex
@@ -158,8 +152,6 @@ PoetryMTEB / MTEB-style **multi-class classification** probing of classical Chin
 }
 ```
 
-Dataset:
-
 ```bibtex
 @data{hou2024classical_theme,
   author = {Hou, Jingrui},
@@ -171,15 +163,15 @@ Dataset:
 }
 ```
 
-This Hub packaging: `PoetryMTEB/ClassicalChinesePoetryThemeClassification` (version 1.0.0)
+This Hub packaging: `PoetryMTEB/ClassicalChinesePoetryThemeClassification` (version 1.0.1)
 
 ## License
 
-**CC0 1.0** (same as the [Dataverse release](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/6QJ7RK)).
+**CC0 1.0** ([Dataverse](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/6QJ7RK)).
 
 ---
 
 # 古典汉语诗歌主题分类（中文说明）
 
-7 类主题：爱情婚姻、交友送别、羁旅思乡、边塞战争、山水田园、咏史怀古、托物言志。  
-划分：train=2624 / test=292（无 validation）。标签 id 为 **0–6**。
+7 类：爱情婚姻、交友送别、羁旅思乡、边塞战争、山水田园、咏史怀古、托物言志。  
+train=2624 / test=292（无 validation）；标签 **0–6**。

VERSION

@@ -1 +1 @@
-1.0.0
+1.0.1

checksums.sha256

@@ -1,16 +1,16 @@
 0aa078488d7fd57d413356ae6268c42166d1c95a4f97fe254f553e6a853ced9b  CITATION.bib
-88cd0fff40676e4df9bb268916d02e1cbb0cb66ee492806c54614f0983de6ad4  CITATION.cff
-4f4aa939b2e4e3c11be90d99159e34c2995f3aec693b7fe839d9c8e76b76500f  data/test-00000-of-00001.parquet
-c229d97d9dce57b753f3fe04f705661059ba34fbe0ef4d75ee44fbe6344818c4  data/train-00000-of-00001.parquet
-879f6d88c73765c983390c273e932d40b283beb09a0eda78b2961994670d71b0  dataset_infos.yaml
-e06e5553a59e6878a0098281c51db7f8c481ccdb84952f5ef00e3ea80d8bde18  label_taxonomy.json
-133fc5d0f5439be9c7ea36f4597e3589fc97a41cc78fd0a22a7734a98bf3ef6e  LICENSE
-a3cc1bfe189b47c21329f122281b8e0f9dacd388249020280dd94adf83d7711d  metadata.json
-d8c66cc61f20daf8581a2520e6d17d98482a89a79d7c49342b306051ecd51c44  README.md
-f8e9e40df258b03a0a6d7ae1e62e1cbdb5fb36e18b5b9ef5018481374e054a08  scripts/evaluate_cc_theme_clf.py
+38c8bc2d73f610ba2ce89cd9dc96b81ff36f35c82a525e5c85ef49c8164a5b0c  CITATION.cff
+f9150ba1da9683c8a4ed5213cde284e3ac74efc93fb1778f76caec2c5dc91f12  data/test-00000-of-00001.parquet
+f7884187fe7fc0130b172acd0e7fa8623605cbab1766e1d23ebbff54365ff885  data/train-00000-of-00001.parquet
+3b9527cb6d1ed76727fa39663766a09f696ef9e3203dd19c85d656da58dd2bf1  dataset_infos.yaml
+723397631d6658366a49ae9c94187811966a161a4af3ef2635fdef34220f5e36  label_taxonomy.json
+02db42bf21e64abd24a1878dda96073493f37ce69953293fb7d372c50ff6b56e  LICENSE
+08e55dd55095f8ef50eb5affa3daa398acb9772bf0b658bd06ed87b181dad6a9  metadata.json
+04d3a9dbedee187b7ee84afe281a471abb5311c0bccfb9211ba0ab9cac4a969e  README.md
+4182ac562a144f534d018d4819e2fcc767167a5cfc181d1975dc303d7055d2dc  scripts/evaluate_cc_theme_clf.py
 058fa4a169c9b0fed806beac7e85119e413115b14415b158199deb0e86904a19  splits/test_ids.json
 2a62bd13efbc96faef06b16e19576ce7b001a441096340cd1f57efac63f300fa  splits/test_ids.txt
 00ca4af6088da7fdc664781c3265594d539148ecf020a2d7fba4419ad9e0dcdf  splits/train_ids.json
 26b878be47e06a87b88b93385746c8bbaa4642dff083289bd65aef5c1553b7e0  splits/train_ids.txt
-99af336f75b9a5da18ed0985a9bed407fcd284535321c9d2e4ec7de3cbc150f1  stats.json
-4ca8bbd3c595ee306226c6996b32b8d47a900217c55a7ed0cfc868ee444f34f1  VERSION
+44676680de82b3e508a13d818a16bb7b2e702e4d15722ea7841c67a484143a87  stats.json
+f34306bb50e2d2ec59c788b9013c5ac682ef7f7f69345a3e957e38c0b6ba2fdd  VERSION

data/test-00000-of-00001.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4f4aa939b2e4e3c11be90d99159e34c2995f3aec693b7fe839d9c8e76b76500f
-size 67610
+oid sha256:f9150ba1da9683c8a4ed5213cde284e3ac74efc93fb1778f76caec2c5dc91f12
+size 67641

data/train-00000-of-00001.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c229d97d9dce57b753f3fe04f705661059ba34fbe0ef4d75ee44fbe6344818c4
-size 651985
+oid sha256:f7884187fe7fc0130b172acd0e7fa8623605cbab1766e1d23ebbff54365ff885
+size 652026

dataset_infos.yaml

@@ -9,8 +9,8 @@ configs:
 dataset_info:
   configs:
   - config_name: default
-    dataset_size: 719595
-    download_size: 719595
+    dataset_size: 719667
+    download_size: 719667
     features:
     - dtype: string
       name: id
@@ -26,8 +26,8 @@ dataset_info:
       name: label_name_zh
     splits:
     - name: train
-      num_bytes: 651985
+      num_bytes: 652026
       num_examples: 2624
     - name: test
-      num_bytes: 67610
+      num_bytes: 67641
       num_examples: 292

label_taxonomy.json

@@ -1,6 +1,5 @@
 {
   "n_classes": 7,
-  "id_scheme": "0-6 matching Dataverse README; Hub 1-7 remapped via label-1",
   "labels": [
     {
       "id": 0,
@@ -54,20 +53,22 @@
   ],
   "counts": {
     "train": {
-      "托物言志": 872,
-      "羁旅思乡": 621,
-      "边塞战争": 318,
-      "交友送别": 269,
-      "咏史怀古": 337,
-      "山水田园": 207
+      "托物言志": 518,
+      "交友送别": 621,
+      "羁旅思乡": 318,
+      "咏史怀古": 354,
+      "爱情婚姻": 269,
+      "山水田园": 337,
+      "边塞战争": 207
     },
     "test": {
-      "羁旅思乡": 74,
-      "托物言志": 86,
-      "交友送别": 43,
-      "咏史怀古": 31,
-      "边塞战争": 35,
-      "山水田园": 23
+      "交友送别": 74,
+      "托物言志": 53,
+      "爱情婚姻": 43,
+      "咏史怀古": 33,
+      "山水田园": 31,
+      "羁旅思乡": 35,
+      "边塞战争": 23
     }
   },
   "source": {

metadata.json

@@ -1,7 +1,7 @@
 {
   "name": "ClassicalChinesePoetryThemeClassification",
   "poetrymteb_repo": "PoetryMTEB/ClassicalChinesePoetryThemeClassification",
-  "version": "1.0.0",
+  "version": "1.0.1",
   "task": "multi-class-classification",
   "language": [
     "zh"
@@ -13,9 +13,6 @@
     "test": 292
   },
   "has_validation": false,
-  "unit": "poem",
-  "label_field": "label",
-  "text_field": "poem",
   "source": {
     "dataverse": "https://doi.org/10.7910/DVN/6QJ7RK",
     "paper_doi": "10.1145/3685679"

scripts/evaluate_cc_theme_clf.py

@@ -20,16 +20,8 @@ def main() -> None:
     p.add_argument("--seed", type=int, default=42)
     p.add_argument("--out-json", default="")
     args = p.parse_args()
-
     ds = load_dataset(args.repo)
     train, test = ds["train"], ds["test"]
-    x_train, y_train = list(train["poem"]), list(train["label"])
-    x_test, y_test = list(test["poem"]), list(test["label"])
-    id2name = {}
-    for lab, name in zip(train["label"], train["label_name"]):
-        id2name[int(lab)] = name
-    target_names = [id2name[i] for i in sorted(id2name)]
-
     clf = Pipeline(
         [
             ("tfidf", TfidfVectorizer(analyzer="char", ngram_range=(1, 3), max_features=50000)),
@@ -41,14 +33,17 @@ def main() -> None:
             ),
         ]
     )
-    clf.fit(x_train, y_train)
-    pred = clf.predict(x_test)
+    clf.fit(list(train["poem"]), list(train["label"]))
+    pred = clf.predict(list(test["poem"]))
+    y_test = list(test["label"])
+    id2name = {int(a): b for a, b in zip(train["label"], train["label_name"])}
+    target_names = [id2name[i] for i in sorted(id2name)]
     metrics = {
         "accuracy": float(accuracy_score(y_test, pred)),
         "macro_f1": float(f1_score(y_test, pred, average="macro")),
         "micro_f1": float(f1_score(y_test, pred, average="micro")),
-        "n_train": len(y_train),
-        "n_test": len(y_test),
+        "n_train": len(train),
+        "n_test": len(test),
         "label_counts_test": dict(Counter(int(x) for x in y_test)),
         "report": classification_report(
             y_test, pred, target_names=target_names, digits=4