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

.
├── {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:

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

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, 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:

@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}
}