cheese-text-distilbert-predictor

Cheese Origin Classification with DistilBERT

Model Description

This repository contains a fine-tuned DistilBERT model for classifying cheese descriptions into their country/region of origin. The model was trained on the aslan-ng/cheese-text dataset as part of Homework 1 for CMU 24-679 (Designing and Deploying AI/ML).

• Base model: distilbert-base-uncased
• Task: Multiclass text classification
• Labels (21 classes): Belgium/Germany, Bulgaria, Cyprus, Denmark, England, France, Germany, Greece, India, Italy, Levant, Mexico, Netherlands, Norway, Peru, Philippines, Poland, Portugal, Spain, Switzerland, USA

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Training and Evaluation

• Train/Val/Test split (augmented data): 640 / 160 / 200
• External validation (original data): 100

The model generalizes well beyond the augmented split, achieving 95% accuracy on the original validation set.

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Error Analysis

Common confusions occur between geographically or culturally close regions:

• Germany vs Switzerland (e.g., Limburger misclassified as Swiss)
• Norway vs Denmark (e.g., Jarlsberg → Denmark)
• Cyprus vs Greece (e.g., Halloumi → Greece)
• Philippines vs Spain (Queso de Bola → Spain)

These errors reflect real-world overlaps in cheese naming and history.

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How to Use

Install dependencies

```bash pip install transformers torch datasets ```

Load model and tokenizer

```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch

model_id = "cassieli226/cheese-text-distilbert-predictor" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForSequenceClassification.from_pretrained(model_id)

text = "Halloumi is a semi-hard Cypriot cheese known for its high melting point." inputs = tokenizer(text, return_tensors="pt")

with torch.no_grad(): logits = model(**inputs).logits predicted_class = logits.argmax(dim=-1).item()

print("Predicted class:", model.config.id2label[predicted_class]) ```

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Dataset

• Source: aslan-ng/cheese-text
• Structure:
  • `original`: 100 manually curated examples
  • `augmented`: 1000 synthetic examples (paraphrased and simplified)

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Intended Use

• Educational demonstration of fine-tuning DistilBERT for multiclass classification.
• Baseline for exploring text augmentation and error analysis in NLP coursework.

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Limitations

• Dataset is small (≈1,100 examples), so predictions may be sensitive to phrasing.
• Cultural and regional overlaps in cheese descriptions can lead to ambiguities.

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Acknowledgments

• Dataset by aslan-ng
• Fine-tuning completed for CMU 24-679 coursework.
• Model training, evaluation, and upload assisted with Hugging Face Transformers.