distilbert_goodreads_book_classification

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distilbert_goodreads_book_classification

This model is a fine-tuned version of distilbert-base-uncased on the Goodreads-Books dataset. It achieves the following results on the evaluation set:

• Loss: 2.0030
• Accuracy: 0.5159
• F1 Score: 0.5065
• Precision: 0.5073
• Recall: 0.5159

Model description

This model is a fine-tuned version of distilbert-base-uncased trained for multi-class book genre classification based on textual metadata (book titles and descriptions).

Due to the noisy and highly heterogeneous nature of crowd-sourced Goodreads user tags, 1,003 unique Goodreads shelves were heuristically mapped into a standardized taxonomy of 31 Amazon Kindle categories. To handle the resulting severe class imbalance, class distribution was balanced using a Random Oversampler before fine-tuning. This model represents a single-stage fine-tuning approach directly on the preprocessed Goodreads metadata dataset, achieving an Accuracy of 50.58% and significantly outperforming traditional vector-based baselines (Doc2Vec + Random Forest at 40.84%).

Intended uses & limitations

• Automated genre classification of books using metadata or descriptions.

• Serving as a baseline for evaluating NLP models on imbalanced, crowd-sourced taxonomy mapping tasks.

• Performance is fundamentally constrained by the inherent noise, ambiguity, and subjectivity of crowdsourced user tags (Goodreads shelves).

• Predictions are mapped specifically to the 31-class Amazon Kindle taxonomy; inputs outside this category scope may yield inaccurate classifications.

Datasets

• Goodreads-Dataset: Hugging Face Repository (Original: BrightData/Goodreads-Books)
• Amazon-Dataset: Kaggle Amazon Kindle Books Dataset)

Training and evaluation data

• Source: Goodreads book metadata and user-generated shelf tags (noisy genres).
• Target Taxonomy: 31 standardized Amazon Kindle categories (genres).
• Input Features: Concatenated description text made of book title, author and summary.

Data Preprocessing & Pipeline

1. Taxonomy Mapping: 1,003 heterogeneous, crowdsourced Goodreads-shelves were heuristically clustered and mapped into a standardized 31-class Amazon Kindle taxonomy to resolve label noise and synonymy.
2. Class Imbalance Handling: To address the severe long-tail class distribution inherent in user-generated book data, Random Oversampling was applied to the training split prior to model fine-tuning.
3. Splits: The dataset was partitioned into stratified (80/20) train and test splits to evaluate the classification performance.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.45.2
• Pytorch 2.5.1
• Datasets 4.1.1
• Tokenizers 0.20.1

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Academic Context & Citation / Akademischer Kontext

This repository and model were developed as part of a Bachelor's thesis in 2026.

• Title: Classification of Goodreads genres: A methodological comparison of Doc2Vec and DistilBERT
• License: CC BY-NC 4.0 (Free for research, education, and personal use; commercial use prohibited)

Dieses Repository und Modell wurden im Rahmen einer Bachelorarbeit im Jahr 2026 entwickelt.

• Titel: Klassifikation von Goodreads-Genres: Ein methodischer Vergleich von Doc2Vec und DistilBERT
• Lizenz: CC BY-NC 4.0 (Frei für Forschung, Lehre und private Nutzung; kommerzielle Nutzung untersagt)