README.md

# MCTC Benchmark Collection

This repository provides a standardized and optimized collection of classic datasets for **Multi-class Text Classification (MCTC)**. Specifically designed for robust evaluation, this collection features a **10-fold cross-validation** setup and specialized metadata for analyzing document and label distributions.

## 🚀 Key Features

* **Standardized Benchmarks**: Includes curated versions of **ACM**, **OHSUMED**, **REUTERS**, and **TWITTER**.
* **10-Fold Cross-Validation**: Every dataset is pre-split into 10 independent folds to ensure statistically significant results and easy reproducibility.
* **Efficient Index-Based Architecture**: A single `samples.pkl` stores the raw content, while splits are managed via lightweight index files (`.pkl`). This ensures high consistency across folds while optimizing storage.
* **Head/Tail Categorization**: Includes specialized metadata (`label_cls.pkl` and `text_cls.pkl`) to distinguish between **Head** and **Tail** entities, enabling deep performance analysis on imbalanced data.
* **Spherical K-Means Clustering**: Each fold includes pre-computed cluster assignments (`text_cluster.h5`) and cluster centroids (`cluster_centroids.h5`), generated via Spherical K-Means on text embeddings. These enable semantic grouping and cluster-aware evaluation.

## 📂 Repository Structure
```text

.

├── {DATASET_NAME}                    # ACM, OHSUMED, REUTERS, TWITTER

│   ├── samples.pkl                   # Global repository: list of dicts with {idx, text, labels}

│   ├── label_cls.pkl                 # Label classification: Defines labels as 'Head' or 'Tail'

│   ├── text_cls.pkl                  # Text classification: Defines samples as 'Head' or 'Tail'

│   ├── relevance_map.pkl             # Ground-truth relevance mapping for classification tasks

│   └── fold_{0..9}                   # 10 independent folds for cross-validation

│       ├── train.pkl                 # Sample indices for training

│       ├── val.pkl                   # Sample indices for validation

│       ├── test.pkl                  # Sample indices for testing

│       ├── labels_descriptions.pkl   # Specific descriptions for labels in this fold

│       ├── cluster_centroids.h5      # Spherical K-Means centroids for this fold

│       └── text_cluster.h5           # Mapping of text_idx → cluster_idx for this fold

└── README.md

```

## 🛠️ Usage

To maintain the exact directory structure and indices, download the repository using the `huggingface_hub`:
```python

from huggingface_hub import snapshot_download

import pickle



# Download the entire repository from the official MCTC link

repo_path = snapshot_download(repo_id="celsofranssa/MCTC", repo_type="dataset")



# Example: Load ACM Samples and Fold 0 Training Split

with open(f"{repo_path}/ACM/samples.pkl", "rb") as f:

    all_samples = pickle.load(f)



with open(f"{repo_path}/ACM/fold_0/train.pkl", "rb") as f:

    train_indices = pickle.load(f)



# Resolve samples

train_set = [all_samples[idx] for idx in train_indices]

print(f"Loaded {len(train_set)} samples for training.")

```

### Loading Cluster Data
```python

import h5py

import numpy as np



# Load cluster centroids for ACM, Fold 0

with h5py.File(f"{repo_path}/ACM/fold_0/cluster_centroids.h5", "r") as f:

    centroids   = f["centroids"][:]     # np.ndarray of shape (n_clusters, dim)

    n_clusters  = f.attrs["n_clusters"]

    dim         = f.attrs["dim"]

    print(f"Centroids shape: {centroids.shape}  |  n_clusters={n_clusters}, dim={dim}")



# Load text → cluster assignments for ACM, Fold 0

with h5py.File(f"{repo_path}/ACM/fold_0/text_cluster.h5", "r") as f:

    text_ids    = f["text_ids"][:]      # np.ndarray of text indices

    cluster_ids = f["cluster_ids"][:]   # np.ndarray of cluster assignments



# Reconstruct the dict {text_idx: cluster_idx}

text_to_cluster = dict(zip(text_ids.tolist(), cluster_ids.tolist()))

print(f"Loaded cluster assignments for {len(text_to_cluster)} texts.")

```

## 📊 Technical Specifications

### Core Metadata Files

* **`label_cls.pkl`**: Essential for analyzing model performance on infrequent labels (Tail) vs. frequent ones (Head).

* **`text_cls.pkl`**: Categorizes documents based on their label frequency composition.
* **`relevance_map.pkl`**: Provides the ground-truth mapping, indicating which labels are relevant for each text sample.



### Cluster Files (per fold)



* **`cluster_centroids.h5`**: Stores the Spherical K-Means centroids fitted on the **development set** (train + val) of each fold.
  * Dataset `centroids`: `float32` array of shape `(n_clusters, dim)`.
  * Attributes: `fold_idx`, `n_clusters`, `dim`.

* **`text_cluster.h5`**: Stores the cluster assignment for every text (train, val, and test) in the fold, resolved against the centroids above.

  * Dataset `text_ids`: integer array of sample indices (gzip-compressed).

  * Dataset `cluster_ids`: integer array of assigned cluster indices (gzip-compressed).

  * Attributes: `fold_idx`, `num_texts`.



### Clustering Methodology



The clusters were produced using **Spherical K-Means** via [FAISS](https://github.com/facebookresearch/faiss), which minimizes Euclidean distance on **L2-normalized** embeddings — an operation mathematically equivalent to maximizing Cosine Similarity. Key design choices:



1. **Fit on development set only** (train + val): centroids are never influenced by test data, preserving evaluation integrity.

2. **Predict on all splits**: train, val, and test texts are all assigned to the nearest centroid via Inner Product search on normalized vectors (`faiss.IndexFlatIP`).

3. **Reproducibility**: a fixed `seed=42` and `niter=32` are used uniformly across all folds and datasets.



## 🎓 Citation



If you use this benchmark collection in your research, please cite our work:

```bibtex

@inproceedings{CelsoFranssa_2025,

  title={Muitas Classes Desbalanceadas? N{\~a}o Classifique-Ranqueie! Uma Abordagem Baseada em Retrieval-Augmented Generation (RAG)-labels para Classifica{\c{c}}{\~a}o Textual Multi-classe},

  author={Fran{\c{c}}a, Celso and Nunes, Ian and Salles, Thiago and Cunha, Washington and Jallais, Gabriel and Rocha, Leonardo and Gon{\c{c}}alves, Marcos Andr{\'e}},

  booktitle={Simp{\'o}sio Brasileiro de Banco de Dados (SBBD)},

  pages={264--277},

  year={2025},

  organization={SBC}

}

```