Model Card for debojit01/course-review-sentiment

Model Details

Model Name: Course Review Sentiment Classifier
Model Type: Text Classification (Multiclass – Positive, Neutral, Negative)
Language: English
License: MIT
Finetuned From: distilbert-base-uncased
Developed By: Debojit Choudhury

Model Description

This model is a fine-tuned DistilBERT model for sentiment classification of course reviews. It predicts whether a review is positive, neutral, or negative, and was trained on a labeled dataset of 100k Coursera reviews.

Uses

Direct Use

This model can be used to:

• Automatically classify course reviews based on sentiment.
• Analyze customer feedback for online education platforms.

Out-of-Scope Use

• Not suitable for non-English text.
• Not suitable for other domains beyond course review sentiment.

How to Get Started with the Model

from transformers import pipeline

classifier = pipeline("text-classification", model="debojit01/course-review-sentiment")
classifier("The course was extremely helpful and well-structured!")

Training Details

Training Data

Kaggle's 100k Coursera Reviews Dataset

• Number of Classes: 3
• Training Framework: Hugging Face Transformers
• Max Seq Length: 512
• Epochs: 3

Evaluation

• Test Split: 20% of full dataset
• Metrics: Accuracy, Macro Precision, Recall, F1
• Macro F1: 0.7647813475266324
• Accuracy: 0.7641242937853108
• Macro Precision: 0.766738569737377
• Macro Recall: 0.7641242937853107
• samples_per_second: 72.966
• steps_per_second: 1.159

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: Tesla T4 GPU (Google Colab)
• Hours used: <2 hours
• Compute Region: US (Colab)

Citation

If you use this model, please cite:

Debojit Choudhury, Course Review Sentiment Classifier (2025), Hugging Face. https://huggingface.co/debojit01/course-review-sentiment