exam-sentiment-distilbert-v1

Exam Sentiment DistilBERT

A fine-tuned DistilBERT model for classifying student exam feedback into three sentiment categories:

• Negative
• Neutral
• Positive

This is an educational NLP project created to learn and demonstrate the complete Hugging Face Transformers workflow, including dataset creation, preprocessing, tokenization, fine-tuning, evaluation, and inference.

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Model Details

• Model type: DistilBERT for sequence classification
• Base model: distilbert-base-uncased
• Task: 3-class text classification
• Language: English
• Number of labels: 3
• Fine-tuned from: distilbert-base-uncased
• Framework: Hugging Face Transformers
• Model version: v1

Label Mapping

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Intended Use

This model is intended for:

• Educational purposes
• Learning NLP and Transformer fine-tuning
• Demonstrating the Hugging Face ecosystem
• Experimenting with exam-feedback sentiment classification
• Understanding model inference and evaluation

The model is not production-ready in its current form.

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Dataset

A custom exam-feedback dataset was created for this project.

Dataset Size

Class Distribution

The dataset contains short English-language statements describing students' experiences and opinions about exams.

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Training

The model was fine-tuned from a pretrained DistilBERT checkpoint.

It was not trained from scratch.

Training Configuration

• Base model: distilbert-base-uncased
• Training samples: 64
• Epochs: 5
• Batch size: 8
• Learning rate: 2e-5
• Weight decay: 0.01
• Environment: Google Colab
• Library: Hugging Face Transformers

Preprocessing

The text was processed using the DistilBERT tokenizer.

The tokenizer converts the text into:

• input_ids
• attention_mask

The sentiment labels were converted into numerical IDs:

Negative → 0
Neutral  → 1
Positive → 2