pavan-ashwayuja/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.

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

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.

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.

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