# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("ysakhale/finetuning_notebook")
model = AutoModelForSequenceClassification.from_pretrained("ysakhale/finetuning_notebook")Quick Links
DistilBERT Fine-tuned on Superhero Texts
Model Summary
- Task: Binary text classification
- Classes: DC vs Marvel
- Base Model: distilbert-base-uncased
- Training Setup: 3 epochs, batch size 16, learning rate 2e-5
- Evaluation Metrics: Accuracy, precision, recall, F1 score
Dataset
- Source: rlogh/superhero-texts
- Split: 70% train, 15% val, 15% test
- Augmented split (~1.1k examples)
Preprocessing
- Tokenization with DistilBERT tokenizer
- Max sequence length: 256
- Labels encoded: DC = 0, Marvel = 1
Results
- Accuracy and F1 reported on test set
- Confusion matrix included in notebook
Error Analysis
The model occasionally misclassifies superheroes with ambiguous or overlapping traits (e.g., similarities between certain DC and Marvel characters).
This suggests the model may rely heavily on explicit universe keywords in the text.
Intended Use
- Educational purpose only
- Demonstration of fine-tuning transformers on text classification
- Not suitable for production deployment
License
Apache-2.0
Hardware/Compute
- Trained on 1 GPU (Colab environment)
- Training time: ~10 minutes
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ysakhale/finetuning_notebook")