| --- |
| language: |
| - en |
| license: mit |
| library_name: transformers |
| tags: |
| - text-classification |
| - distilbert |
| - book-genre-classification |
| - mlops |
| datasets: |
| - custom |
| metrics: |
| - accuracy |
| - f1 |
| pipeline_tag: text-classification |
| model-index: |
| - name: ML_OPS_ASSIGNMENT2 |
| results: |
| - task: |
| type: text-classification |
| name: Text Classification |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.5831 |
| - name: F1 (weighted) |
| type: f1 |
| value: 0.5810 |
| --- |
| |
| # DistilBERT Book Genre Classifier |
|
|
| A fine-tuned **DistilBERT** model for classifying book reviews into 8 genres. |
|
|
| ## Model Description |
|
|
| This model is based on `distilbert-base-cased` and was fine-tuned on the UCSD Goodreads book reviews dataset. It classifies a given book review text into one of 8 genres. |
|
|
| - **Model:** distilbert-base-cased |
| - **Task:** Multi-class text classification (8 genres) |
| - **Language:** English |
| - **License:** MIT |
|
|
| ## Supported Genres |
|
|
| | Label | Genre | |
| |-------|-------| |
| | 0 | Children | |
| | 1 | Comics & Graphic | |
| | 2 | Fantasy & Paranormal | |
| | 3 | History & Biography | |
| | 4 | Mystery, Thriller & Crime | |
| | 5 | Poetry | |
| | 6 | Romance | |
| | 7 | Young Adult | |
|
|
| ## Training Details |
|
|
| | Parameter | Value | |
| |-----------|-------| |
| | Base model | distilbert-base-cased | |
| | Epochs | 3 | |
| | Batch size (train) | 16 | |
| | Batch size (eval) | 32 | |
| | Learning rate | 3e-5 | |
| | Warmup steps | 100 | |
| | Weight decay | 0.01 | |
| | Max sequence length | 512 | |
| | Train samples | 6,400 | |
| | Test samples | 1,600 | |
| | Platform | Kaggle (GPU T4 x2) | |
| | Tracking | Weights & Biases | |
|
|
| ## Results |
|
|
| | Metric | Score | |
| |--------|-------| |
| | Accuracy | 0.5831 | |
| | F1 Score (weighted) | 0.5810 | |
| | Eval Loss | 2.2847 | |
|
|
| ### Per-Epoch Results |
|
|
| | Epoch | Training Loss | Validation Loss | Accuracy | F1 | |
| |-------|--------------|-----------------|----------|-----| |
| | 1 | 2.5710 | 2.5337 | 0.5525 | 0.5454 | |
| | 2 | 2.1273 | 2.2859 | 0.5981 | 0.5983 | |
| | 3 | 1.6126 | 2.2923 | 0.6094 | 0.6089 | |
|
|
| ## How to Use |
|
|
| ```python |
| from transformers import pipeline |
| |
| classifier = pipeline( |
| "text-classification", |
| model="sureshbabugandla/ML_OPS_ASSIGNMENT2" |
| ) |
| |
| result = classifier("This book was a thrilling mystery with unexpected twists.") |
| print(result) |
| ``` |
|
|
| Or load the model and tokenizer separately: |
|
|
| ```python |
| from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification |
| |
| tokenizer = DistilBertTokenizerFast.from_pretrained("sureshbabugandla/ML_OPS_ASSIGNMENT2") |
| model = DistilBertForSequenceClassification.from_pretrained("sureshbabugandla/ML_OPS_ASSIGNMENT2") |
| ``` |
|
|
| ## Dataset |
|
|
| The model was trained on the [UCSD Book Graph](https://mengtingwan.github.io/data/goodreads.html) dataset, which contains book reviews from Goodreads across multiple genres. 2,000 reviews were sampled from each of the 8 genres, split into 800 train and 200 test samples per genre. |
|
|
| ## Developed By |
|
|
| - **Name:** Suresh Babu Gandla |
| - **Roll Number:** G25AIT2119 |
|
|
| ## Links |
|
|
| - **GitHub:** https://github.com/g25ait2119/MLOpsAssignment2 |
| - **W&B Dashboard:** https://wandb.ai/g25ait2119-sureshbabu-gandla/mlops-assignment2 |
| - **Kaggle Notebook:** https://www.kaggle.com/code/sureshbabugandla/mlops-a2-training |
|
|
