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mirko-doljanica/
distilbert-imdb-sentiment

Name: distilbert-imdb-sentiment
Availability: InStock

text-embeddings-inference

endpoints_compatible

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🎬 DistilBERT IMDb Sentiment Classifier

This model is a fine-tuned version of DistilBERT for sentiment analysis on the IMDb movie reviews dataset.
It classifies reviews into positive or negative sentiment with high accuracy.

📊 Model Details

Model type: Transformer-based model (DistilBERT)
Task: Sentiment Analysis / Text Classification
Framework: Hugging Face Transformers (PyTorch)
Dataset: IMDb (50,000+ labeled movie reviews)
Accuracy: ~91.5% on test data

📁 Usage

You can load and use this model directly with transformers:

python

from transformers import pipeline

sentiment_pipeline = pipeline("sentiment-analysis", model="mirko-doljanica/distilbert-imdb-sentiment")
print(sentiment_pipeline("I loved this movie! The performances were amazing."))

## 📈 Results

| Metric       | Score |
|--------------|-------|
| Accuracy     | 0.915 |
| Precision    | 0.915 |
| Recall       | 0.915 |
| F1-score     | 0.915 |

These results were achieved after fine-tuning DistilBERT on the IMDb dataset with 2 epochs and a batch size of 8. The model performs consistently well on binary sentiment classification tasks.

📦 Applications

📊 Review Analysis: Automatically classify product, movie, or app reviews as positive or negative.
💬 Social Media Monitoring: Analyze public sentiment from platforms like Twitter, Reddit, or forums.
🧠 Customer Feedback Analysis: Segment user feedback into positive and negative categories for better decision-making.
🔍 Opinion Mining: Detect trends in public opinion about topics, brands, or events.
🧪 Research & Prototyping: Quickly integrate into NLP workflows for experimentation and benchmarking.

⚠️ Limitations & Bias

🗣️ Language Restriction: The model is trained on English text. Performance will degrade significantly for other languages without further fine-tuning.
🎬 Domain Bias: Since the training data consists of movie reviews, performance might vary when used for other domains like finance, healthcare, or politics.
🤖 Data Bias: The dataset may contain subjective or biased opinions reflecting societal stereotypes present in IMDb reviews.
📉 Nuance Handling: The model might struggle with sarcasm, humor, or context-heavy statements that require deeper semantic understanding.

🛠️ How It Was Built

This model was built by fine-tuning the pre-trained DistilBERT model using the IMDb movie reviews dataset.
The training process involved:

Preprocessing text (tokenization, truncation, padding)
Splitting into training and validation sets
Fine-tuning for 2 epochs with a batch size of 8
Evaluation on the test set to measure accuracy, precision, recall, and F1-score

Training was done with the Hugging Face transformers, datasets, and accelerate libraries, leveraging PyTorch as the backend.

📚 Citation

If you use this model in your research, please cite it as follows: @misc{distilbert_imdb_sentiment, author = {Mirko Doljanica}, title = {DistilBERT IMDb Sentiment Classifier}, year = {2025}, howpublished = {\url{https://huggingface.co/mirko-doljanica/distilbert-imdb-sentiment}} }

📬 Contact

Author: Mirko Doljanica

🌐 Hugging Face: mirko-doljanica
💻 GitHub: mirko-doljanica
📧 Email: [doljanicamir@gmail.com]

Feel free to open issues, contribute, or reach out for collaborations.