Add TamilSangamLiteraryDeviceClassification (8 merged devices; stratified train/test)

README.md

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+---
+license: other
+task_categories:
+- text-classification
+task_ids:
+- multi-class-classification
+language:
+- ta
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+pretty_name: TamilSangamLiteraryDeviceClassification
+tags:
+- poetry
+- tamil
+- sangam
+- literary-device
+- rhetoric
+- multi-class-classification
+- mteb
+- poetrymteb
+- embedding-evaluation
+annotations_creators:
+- expert-generated
+source_datasets:
+- Tamil-Sangam-Literature-Dataset
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
+  default: true
+dataset_info:
+- config_name: default
+  features:
+  - name: id
+    dtype: string
+  - name: line_number
+    dtype: int64
+  - name: poem
+    dtype: string
+  - name: transliteration
+    dtype: string
+  - name: english
+    dtype: string
+  - name: label
+    dtype: int64
+  - name: label_name
+    dtype: string
+  - name: original_device
+    dtype: string
+  splits:
+  - name: train
+    num_examples: 840
+  - name: test
+    num_examples: 211
+---
+
+# TamilSangamLiteraryDeviceClassification
+
+Multi-class **literary device / rhetoric classification** for classical **Tamil Sangam** verse lines (PoetryMTEB), derived from the [Tamil Sangam Literature Dataset](https://github.com/kameshkanna/Tamil-Sangam-Literature-Dataset).
+
+Upstream `Literary Devices` strings are noisy (44 free-text labels, heavily skewed to `Imagery`). We **merge** near-duplicates and rare labels into a closed **8-class** taxonomy, then create a stratified train/test split for embedding evaluation.
+
+## Dataset Card
+
+| Item | Description |
+|------|-------------|
+| **Source** | [Tamil Sangam Literature Dataset](https://github.com/kameshkanna/Tamil-Sangam-Literature-Dataset) (`Tamil Dangam.csv`) |
+| **Languages** | Tamil (`ta`); English translation & Latin transliteration kept as auxiliaries |
+| **Unit** | Verse **line** (`poem` = Original Tamil Text) |
+| **Labels** | **8** merged literary-device classes |
+| **Size** | train=840; test=211 (dropped empty text/label: 1) |
+| **Splits** | Stratified by merged label ≈ 80% / 20%, seed=42 |
+| **License** | Upstream repository terms (no explicit SPDX; research use — attribute source) |
+| **Evaluation metrics** | Classification on embeddings: **accuracy**, **macro/micro F1** |
+
+## Label taxonomy (8)
+
+| id | label_name | zh | gloss | train | test | total |
+|---:|------------|----|-------|------:|-----:|------:|
+| 0 | `Imagery` | 意象描写 | Imagery and related visual/emotional descriptive imagery | 486 | 122 | 608 |
+| 1 | `Simile` | 明喻 | Simile | 102 | 26 | 128 |
+| 2 | `Descriptive` | 描述 | Descriptive / setting description | 91 | 23 | 114 |
+| 3 | `Narrative` | 叙事 | Narrative / narrative reflection | 41 | 10 | 51 |
+| 4 | `DirectAddress` | 直接称呼 | Direct address to a hearer / addressee | 36 | 9 | 45 |
+| 5 | `Metaphor` | 隐喻 | Metaphor | 26 | 6 | 32 |
+| 6 | `RhetoricalQuestion` | 反问/设问 | Rhetorical question / question | 11 | 3 | 14 |
+| 7 | `Other` | 其他修辞 | Merged rare devices (symbolism, personification, hyperbole, etc.) | 47 | 12 | 59 |
+
+Codebook: `label_taxonomy.json`.
+
+### Merge rules (upstream → merged)
+
+| original_device | merged label |
+|-----------------|--------------|
+| `Description` | `Descriptive` |
+| `Descriptive` | `Descriptive` |
+| `Setting` | `Descriptive` |
+| `Direct Address` | `DirectAddress` |
+| `Direct address` | `DirectAddress` |
+| `Descriptive imagery` | `Imagery` |
+| `Emotional imagery` | `Imagery` |
+| `Imagery` | `Imagery` |
+| `Visual imagery` | `Imagery` |
+| `Metaphor` | `Metaphor` |
+| `Narrative` | `Narrative` |
+| `Narrative Reflection` | `Narrative` |
+| `Admonition` | `Other` |
+| `Conditional` | `Other` |
+| `Conditional Statement` | `Other` |
+| `Conditional statement` | `Other` |
+| `Contrast` | `Other` |
+| `Dialogue` | `Other` |
+| `Direct Speech` | `Other` |
+| `Direct speech` | `Other` |
+| `Direct statement` | `Other` |
+| `Emotional Expression` | `Other` |
+| `Encouragement` | `Other` |
+| `Exclamation` | `Other` |
+| `Expression of Suffering` | `Other` |
+| `Expression of longing` | `Other` |
+| `Expression of well-wishes` | `Other` |
+| `Historical Reference` | `Other` |
+| `Hyperbole` | `Other` |
+| `Hypothetical` | `Other` |
+| `Irony` | `Other` |
+| `Negative statement` | `Other` |
+| `Personification` | `Other` |
+| `Philosophical Reflection` | `Other` |
+| `Quotation` | `Other` |
+| `Reference` | `Other` |
+| `Repetition` | `Other` |
+| `Symbolism` | `Other` |
+| `Theme` | `Other` |
+| `Wish` | `Other` |
+| `Question` | `RhetoricalQuestion` |
+| `Rhetorical Question` | `RhetoricalQuestion` |
+| `Rhetorical question` | `RhetoricalQuestion` |
+| `Simile` | `Simile` |
+
+Any upstream string not listed above is mapped to **`Other`**.
+
+**Rationale:** `Imagery` alone covers ~57% of rows; many remaining labels have ≤4 examples and cannot support a reliable test split. Merging yields a compact, imbalanced-but-usable 8-way task.
+
+## Features
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `id` | string | Example id (`sangam-device-{line_number}`) |
+| `line_number` | int64 | Upstream `Line Number` |
+| `poem` | string | **Classification input**: Original Tamil Text |
+| `transliteration` | string | Latin-script transliteration (auxiliary) |
+| `english` | string | English translation (auxiliary) |
+| `label` | int64 | Merged class id (0–7) |
+| `label_name` | string | Canonical English class name |
+| `original_device` | string | Raw upstream `Literary Devices` string (before merge) |
+
+## Construction method
+
+1. Load `Tamil Dangam.csv` (1052 lines).
+2. Map `Literary Devices` → 8-way taxonomy via the merge table above.
+3. Drop rows with empty Tamil text or missing device label.
+4. Stratified train/test split by `label_name` (seed=42, ≈ 80/19).
+
+## How to load
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("PoetryMTEB/TamilSangamLiteraryDeviceClassification")
+print(ds["train"][0]["poem"])
+print(ds["train"][0]["label_name"], ds["train"][0]["original_device"])
+```
+
+## Intended use / limitations
+
+- PoetryMTEB multi-class probing of **Tamil poetic line** embeddings for rhetorical style.
+- Labels inherit upstream annotation quality; `Imagery` remains dominant after merge.
+- `Other` is a residual bucket — do not over-interpret fine-grained rhetoric inside it.
+- Companion bitext release: `PoetryMTEB/TamilSangamBitextMining`.
+
+## Citation / provenance
+
+Please attribute the upstream dataset:
+
+- https://github.com/kameshkanna/Tamil-Sangam-Literature-Dataset
+- This Hub packaging: `PoetryMTEB/TamilSangamLiteraryDeviceClassification`
+
+## License
+
+Follow the upstream GitHub repository terms. This packaging redistributes line texts and merged device labels for research evaluation under PoetryMTEB.

data/test-00000-of-00001.parquet

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+oid sha256:44a54c5c210136497322f19e4f51627a9c70e82eb66cdf14a9c94438a7c529e5
+size 32798

data/train-00000-of-00001.parquet

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+oid sha256:52422e4a6231a87f835eb006abc95ab63955a7e4b6e84f17b4dfbb5d8a5af554
+size 104636

dataset_infos.yaml

@@ -0,0 +1,37 @@
+configs:
+- config_name: default
+  data_files:
+  - path: data/train-*.parquet
+    split: train
+  - path: data/test-*.parquet
+    split: test
+  default: true
+dataset_info:
+  configs:
+  - config_name: default
+    dataset_size: 137434
+    download_size: 137434
+    features:
+    - dtype: string
+      name: id
+    - dtype: int64
+      name: line_number
+    - dtype: string
+      name: poem
+    - dtype: string
+      name: transliteration
+    - dtype: string
+      name: english
+    - dtype: int64
+      name: label
+    - dtype: string
+      name: label_name
+    - dtype: string
+      name: original_device
+    splits:
+    - name: train
+      num_bytes: 104636
+      num_examples: 840
+    - name: test
+      num_bytes: 32798
+      num_examples: 211

label_taxonomy.json

@@ -0,0 +1,137 @@
+{
+  "n_classes": 8,
+  "labels": [
+    {
+      "id": 0,
+      "name": "Imagery",
+      "name_zh": "意象描写",
+      "gloss": "Imagery and related visual/emotional descriptive imagery"
+    },
+    {
+      "id": 1,
+      "name": "Simile",
+      "name_zh": "明喻",
+      "gloss": "Simile"
+    },
+    {
+      "id": 2,
+      "name": "Descriptive",
+      "name_zh": "描述",
+      "gloss": "Descriptive / setting description"
+    },
+    {
+      "id": 3,
+      "name": "Narrative",
+      "name_zh": "叙事",
+      "gloss": "Narrative / narrative reflection"
+    },
+    {
+      "id": 4,
+      "name": "DirectAddress",
+      "name_zh": "直接称呼",
+      "gloss": "Direct address to a hearer / addressee"
+    },
+    {
+      "id": 5,
+      "name": "Metaphor",
+      "name_zh": "隐喻",
+      "gloss": "Metaphor"
+    },
+    {
+      "id": 6,
+      "name": "RhetoricalQuestion",
+      "name_zh": "反问/设问",
+      "gloss": "Rhetorical question / question"
+    },
+    {
+      "id": 7,
+      "name": "Other",
+      "name_zh": "其他修辞",
+      "gloss": "Merged rare devices (symbolism, personification, hyperbole, etc.)"
+    }
+  ],
+  "merge_map": {
+    "Imagery": "Imagery",
+    "Visual imagery": "Imagery",
+    "Emotional imagery": "Imagery",
+    "Descriptive imagery": "Imagery",
+    "Simile": "Simile",
+    "Descriptive": "Descriptive",
+    "Description": "Descriptive",
+    "Setting": "Descriptive",
+    "Narrative": "Narrative",
+    "Narrative Reflection": "Narrative",
+    "Direct Address": "DirectAddress",
+    "Direct address": "DirectAddress",
+    "Metaphor": "Metaphor",
+    "Rhetorical Question": "RhetoricalQuestion",
+    "Rhetorical question": "RhetoricalQuestion",
+    "Question": "RhetoricalQuestion",
+    "Symbolism": "Other",
+    "Direct statement": "Other",
+    "Personification": "Other",
+    "Hyperbole": "Other",
+    "Emotional Expression": "Other",
+    "Conditional": "Other",
+    "Contrast": "Other",
+    "Direct Speech": "Other",
+    "Direct speech": "Other",
+    "Dialogue": "Other",
+    "Negative statement": "Other",
+    "Reference": "Other",
+    "Historical Reference": "Other",
+    "Quotation": "Other",
+    "Theme": "Other",
+    "Philosophical Reflection": "Other",
+    "Repetition": "Other",
+    "Conditional Statement": "Other",
+    "Conditional statement": "Other",
+    "Hypothetical": "Other",
+    "Expression of well-wishes": "Other",
+    "Expression of longing": "Other",
+    "Expression of Suffering": "Other",
+    "Wish": "Other",
+    "Encouragement": "Other",
+    "Admonition": "Other",
+    "Irony": "Other",
+    "Exclamation": "Other"
+  },
+  "default_unmapped": "Other",
+  "counts": {
+    "train": {
+      "Imagery": 486,
+      "Simile": 102,
+      "Descriptive": 91,
+      "Narrative": 41,
+      "Other": 47,
+      "Metaphor": 26,
+      "DirectAddress": 36,
+      "RhetoricalQuestion": 11
+    },
+    "test": {
+      "Imagery": 122,
+      "DirectAddress": 9,
+      "Narrative": 10,
+      "Descriptive": 23,
+      "Simile": 26,
+      "Other": 12,
+      "RhetoricalQuestion": 3,
+      "Metaphor": 6
+    },
+    "all": {
+      "Other": 59,
+      "Descriptive": 114,
+      "Metaphor": 32,
+      "Imagery": 608,
+      "Simile": 128,
+      "DirectAddress": 45,
+      "Narrative": 51,
+      "RhetoricalQuestion": 14
+    }
+  },
+  "split": {
+    "train_ratio": 0.8,
+    "seed": 42,
+    "stratify": "label_name"
+  }
+}