Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Chima207/distilbert_amazon_goodreads_book_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chima207/distilbert_amazon_goodreads_book_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Chima207/distilbert_amazon_goodreads_book_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Chima207/distilbert_amazon_goodreads_book_classification") model = AutoModelForSequenceClassification.from_pretrained("Chima207/distilbert_amazon_goodreads_book_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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- Transformers 4.45.2
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- Pytorch 2.5.1
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- Datasets 4.1.1
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- Tokenizers 0.20.1
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- Transformers 4.45.2
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- Pytorch 2.5.1
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- Tokenizers 0.20.1
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---
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## Academic Context & Citation / Akademischer Kontext
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This repository and model were developed as part of a Bachelor's thesis in 2026.
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* **Title:** Classification of Goodreads genres: A methodological comparison of Doc2Vec and DistilBERT
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* **License:** [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) (Free for research, education, and personal use; commercial use prohibited)
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Dieses Repository und Modell wurden im Rahmen einer Bachelorarbeit im Jahr 2026 entwickelt.
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* **Titel:** Klassifikation von Goodreads-Genres: Ein methodischer Vergleich von Doc2Vec und DistilBERT
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* **Lizenz:** [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) (Frei für Forschung, Lehre und private Nutzung; kommerzielle Nutzung untersagt)
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