Add DISCOPALAffectClassification (5 emotions; z>=1+top-1; train/test)

README.md

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+---
+license: apache-2.0
+task_categories:
+- text-classification
+task_ids:
+- multi-label-classification
+language:
+- es
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+pretty_name: DISCOPALAffectClassification
+tags:
+- poetry
+- spanish
+- sonnet
+- affect
+- emotion-classification
+- multi-label-classification
+- disco
+- disco-pal
+- mteb
+- poetrymteb
+- embedding-evaluation
+annotations_creators:
+- expert-generated
+source_datasets:
+- DISCO PAL
+- DISCO
+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: source_index
+    dtype: int64
+  - name: poem
+    dtype: string
+  - name: labels
+    sequence: int64
+  - name: label_names
+    sequence: string
+  - name: scores
+    sequence: float64
+  - name: dimensional_scores
+    sequence: float64
+  - name: dimensional_names
+    sequence: string
+  - name: n_annotators
+    dtype: int64
+  splits:
+  - name: train
+    num_examples: 219
+  - name: test
+    num_examples: 55
+---
+
+# DISCOPALAffectClassification
+
+Multi-label **affect / emotion classification** over **Spanish sonnets** for PoetryMTEB, derived from [DISCO PAL](https://arxiv.org/abs/2007.04626) (Barbado et al., 2020).
+
+Each poem is a classical **soneto** from the diachronic Spanish sonnet corpus [DISCO](https://github.com/pruizf/disco), annotated by three POSTDATA (UNED) domain experts for evoked affect on a **1–4** intensity scale. We aggregate with the official **median** file (`poems_corpus_all.csv`) and binarize the five basic emotions for embedding evaluation.
+
+## Dataset Card
+
+| Item | Description |
+|------|-------------|
+| **Source annotations** | [DISCO PAL](https://github.com/AlbertoBarbado/DISCO_PAL) (`Processed Annotations/poems_corpus_all.csv`) |
+| **Source poems** | [DISCO](https://github.com/pruizf/disco) (Diachronic Spanish Sonnet Corpus) |
+| **Paper** | Barbado et al., *DISCO PAL: Diachronic Spanish Sonnet Corpus with Psychological and Affective Labels*, [arXiv:2007.04626](https://arxiv.org/abs/2007.04626) |
+| **Languages** | Spanish (`es`) |
+| **Unit** | Full sonnet text (`poem`) |
+| **Labels** | Multi-label subset of **5** basic emotions (Happiness, Sadness, Anger, Fear, Disgust) |
+| **Score scale** | Continuous intensities **1–4** (evoked affect); median of 3 experts |
+| **Size** | train=219; test=55 |
+| **Splits** | Stratified by primary (highest-score) affect ≈ 80% / 20%, seed=42 |
+| **License** | [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0) (DISCO PAL project) |
+| **Evaluation metrics** | Multi-label classification on embeddings: **macro/micro F1**, **Average Precision (AP)** |
+
+## Label binarization
+
+Absolute thresholds on the 1–4 scale leave many poems with **no** positive emotion (e.g. score ≥ 3 empties ~45% of poems). Following the PoetryMTEB POCA pipeline, we therefore use **relative** within-poem salience:
+
+1. Take the five emotion intensities (median over experts).
+2. Compute **within-poem z-scores**; keep emotions with \(z \ge 1.0\).
+3. Always include the **top-1** emotion (guarantees ≥1 label).
+
+Mean labels/poem ≈ **1.22**.
+
+Auxiliary **dimensional** ratings (Valence, Arousal, Concreteness, Imageability, Context Availability) are stored as continuous scores for analysis; they are **not** classification targets in this release.
+
+## Label taxonomy (5)
+
+| id | label_name (en) | es | zh | train | test | total |
+|---:|-----------------|----|----|------:|-----:|------:|
+| 0 | `Happiness` | Alegría | 喜悦 | 86 | 22 | 108 |
+| 1 | `Sadness` | Tristeza | 悲伤 | 125 | 33 | 158 |
+| 2 | `Anger` | Ira | 愤怒 | 14 | 4 | 18 |
+| 3 | `Fear` | Miedo | 恐惧 | 10 | 2 | 12 |
+| 4 | `Disgust` | Asco | 厌恶 | 30 | 8 | 38 |
+
+Codebook: `label_taxonomy.json`.
+
+### Dimensional scores (auxiliary)
+
+| name (en) | es | zh | scale |
+|-----------|----|----|-------|
+| `Valence` | Valencia | 效价 | 1–4 (median of 3 experts) |
+| `Arousal` | Activación | 唤醒度 | 1–4 (median of 3 experts) |
+| `Concreteness` | Concreción | 具体性 | 1–4 (median of 3 experts) |
+| `Imageability` | Imaginabilidad | 意象性 | 1–4 (median of 3 experts) |
+| `ContextAvailability` | Disponibilidad contextual | 语境可得性 | 1–4 (median of 3 experts) |
+
+## Features
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `id` | string | Example id (`discopal-affect-{source_index}`) |
+| `source_index` | int64 | Row index in upstream `poems_corpus_all.csv` |
+| `poem` | string | Full Spanish sonnet text (classification input) |
+| `labels` | list[int64] | Emotion class indices |
+| `label_names` | list[string] | Canonical English emotion names |
+| `scores` | list[float64] | Median intensities for the 5 emotions (taxonomy order, 1–4) |
+| `dimensional_scores` | list[float64] | Median dimensional ratings (order = `dimensional_names`) |
+| `dimensional_names` | list[string] | Names of dimensional dimensions |
+| `n_annotators` | int64 | Number of experts aggregated (3; median) |
+
+## Construction method
+
+1. Load `poems_corpus_all.csv` (median of annotators a1/a2/a3).
+2. Normalize column names (strip whitespace / NBSP artifacts).
+3. Map source columns (`happinness` typo preserved upstream → `Happiness`) to the 5-emotion taxonomy.
+4. Binarize with within-poem \(z \ge 1.0\) + top-1.
+5. Stratified train/test split by primary affect (seed=42).
+
+## Intended use
+
+Designed for **PoetryMTEB / MTEB-style** multi-label classification probing of poem embeddings in Spanish. Not a clinical diagnostic resource; psychological companion labels are released separately as `DISCOPALPsychClassification`.
+
+## Citation
+
+```bibtex
+@article{barbado2020disco,
+  title={DISCO PAL: Diachronic Spanish Sonnet Corpus with Psychological and Affective Labels},
+  author={Barbado, Alberto and Fresno, Víctor and Riesco, Ángeles Manjarrés and Ros, Salvador},
+  journal={arXiv preprint arXiv:2007.04626},
+  year={2020}
+}
+```
+
+Please also cite the underlying sonnet corpus:
+
+```bibtex
+@misc{disco2017,
+  author={Ruiz Fabo, Pablo and Bermúdez Sabel, Helena and Martínez Cantón, Clara and Calvo Tello, José},
+  title={Diachronic Spanish Sonnet Corpus (DISCO)},
+  year={2017},
+  howpublished={UNED / Zenodo},
+  doi={10.5281/zenodo.1069844},
+  url={https://github.com/pruizf/disco}
+}
+```
+
+## License
+
+Apache License 2.0 for DISCO PAL annotations and this redistribution. Poem texts originate from DISCO; respect upstream DISCO terms when redistributing full texts beyond research evaluation use.

data/test-00000-of-00001.parquet

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+size 29320

data/train-00000-of-00001.parquet

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dataset_infos.yaml

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+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: 122164
+    download_size: 122164
+    features:
+    - dtype: string
+      name: id
+    - dtype: int64
+      name: source_index
+    - dtype: string
+      name: poem
+    - sequence:
+        dtype: int64
+      name: labels
+    - sequence:
+        dtype: string
+      name: label_names
+    - sequence:
+        dtype: float64
+      name: scores
+    - sequence:
+        dtype: float64
+      name: dimensional_scores
+    - sequence:
+        dtype: string
+      name: dimensional_names
+    - dtype: int64
+      name: n_annotators
+    splits:
+    - name: train
+      num_bytes: 92844
+      num_examples: 219
+    - name: test
+      num_bytes: 29320
+      num_examples: 55

label_taxonomy.json

@@ -0,0 +1,105 @@
+{
+  "n_classes": 5,
+  "labels": [
+    {
+      "id": 0,
+      "name": "Happiness",
+      "name_es": "Alegría",
+      "name_zh": "喜悦",
+      "source_column": "happinness"
+    },
+    {
+      "id": 1,
+      "name": "Sadness",
+      "name_es": "Tristeza",
+      "name_zh": "悲伤",
+      "source_column": "sadness"
+    },
+    {
+      "id": 2,
+      "name": "Anger",
+      "name_es": "Ira",
+      "name_zh": "愤怒",
+      "source_column": "anger"
+    },
+    {
+      "id": 3,
+      "name": "Fear",
+      "name_es": "Miedo",
+      "name_zh": "恐惧",
+      "source_column": "fear"
+    },
+    {
+      "id": 4,
+      "name": "Disgust",
+      "name_es": "Asco",
+      "name_zh": "厌恶",
+      "source_column": "disgust"
+    }
+  ],
+  "dimensional": [
+    {
+      "name": "Valence",
+      "name_es": "Valencia",
+      "name_zh": "效价",
+      "source_column": "valence"
+    },
+    {
+      "name": "Arousal",
+      "name_es": "Activación",
+      "name_zh": "唤醒度",
+      "source_column": "arousal"
+    },
+    {
+      "name": "Concreteness",
+      "name_es": "Concreción",
+      "name_zh": "具体性",
+      "source_column": "concreteness"
+    },
+    {
+      "name": "Imageability",
+      "name_es": "Imaginabilidad",
+      "name_zh": "意象性",
+      "source_column": "imageability"
+    },
+    {
+      "name": "ContextAvailability",
+      "name_es": "Disponibilidad contextual",
+      "name_zh": "语境可得性",
+      "source_column": "context availability"
+    }
+  ],
+  "binarization": {
+    "rule": "within-poem z>=1.0 + always include top-1",
+    "score_aggregation": "median of 3 expert annotators",
+    "score_scale": "1-4"
+  },
+  "counts": {
+    "train": {
+      "Happiness": 86,
+      "Sadness": 125,
+      "Fear": 10,
+      "Disgust": 30,
+      "Anger": 14
+    },
+    "test": {
+      "Happiness": 22,
+      "Sadness": 33,
+      "Disgust": 8,
+      "Fear": 2,
+      "Anger": 4
+    },
+    "all": {
+      "Happiness": 108,
+      "Sadness": 158,
+      "Anger": 18,
+      "Disgust": 38,
+      "Fear": 12
+    }
+  },
+  "split": {
+    "train_ratio": 0.8,
+    "seed": 42,
+    "stratify": "primary_affect"
+  }
+}