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---
library_name: transformers
tags:
- nlp
- text
- multiclass
- classification
license: mit
base_model:
- distilbert/distilbert-base-uncased
---
# 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
```python
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
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **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 |