Chima207/distilbert_amazon_book_classification

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distilbertamazonbook_classification

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

• —Loss: 1.4475
• —Accuracy: 0.5871
• —F1 Score: 0.5865
• —Precision: 0.5967
• —Recall: 0.5871

Model description

This model is a fine-tuned version of distilbert-base-uncased trained directly on structured Amazon book metadata across 31 standardized Kindle categories.

Serving as a clean-data benchmark in comparative analysis, this model evaluates genre classification performance under structured, editorial metadata conditions. It achieves an Accuracy of 58.71% and a Macro F1-Score of 58.65%, demonstrating that high inherent data quality and structured category labels significantly improve the upper-bound performance of transformer architectures.

Intended uses & limitations

• —Direct classification of structured book descriptions into standard Amazon Kindle categories.

• —Comparative benchmarking for domain adaptation and cross-domain transfer learning experiments.

• —May underperform or show sensitivity when applied to highly informal, uncurated, or user-generated text inputs without prior domain adaptation.

Datasets

• —Amazon-Dataset: [Kaggle Amazon Kindle Books Dataset](https://www.kaggle.com/datasets/asaniczka/amazon-kindle-books-dataset-2023-130k-books))

Training and evaluation data

• —Source: Official Amazon Kindle book product listings and metadata.
• —Target Taxonomy: 31 standardized Amazon Kindle categories (e.g., Literature & Fiction, Sci-Fi & Fantasy, Romance, Business & Money).
• —Input Features: Concatenated book title and author.

• —Metadata Quality: High quality, structured, and editorially curated product descriptions with minimal noise compared to community-driven tags.
• —Splits: Partitioned into stratified (80/20) train and test sets across all 31 target classes.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 2e-05
• —trainbatchsize: 4
• —evalbatchsize: 4
• —seed: 42
• —gradientaccumulationsteps: 4
• —totaltrainbatch_size: 16
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —num_epochs: 2
• —mixedprecisiontraining: Native AMP

Training results

Framework versions

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

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)