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--- |
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language: en |
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tags: |
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- sentiment-analysis |
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- modernbert |
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- imdb |
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datasets: |
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- imdb |
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metrics: |
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- accuracy |
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- f1 |
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title: IMDb Sentiment Analyzer |
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emoji: 🤗 |
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colorFrom: blue |
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colorTo: green |
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sdk: gradio |
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sdk_version: "5.29.0" |
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app_file: app.py |
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pinned: false |
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hf_oauth: false |
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disable_embedding: false |
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--- |
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# ModernBERT IMDb Sentiment Analysis Model |
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## Model Description |
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Fine-tuned ModernBERT model for sentiment analysis on IMDb movie reviews. Achieves 95.75% accuracy on the test set. |
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## Usage |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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model = AutoModelForSequenceClassification.from_pretrained("voxmenthe/modernbert-imdb-sentiment") |
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tokenizer = AutoTokenizer.from_pretrained("answerdotai/ModernBERT-base") |
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# Input processing |
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inputs = tokenizer("This movie was fantastic!", return_tensors="pt") |
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outputs = model(**inputs) |
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# Get the predicted class |
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predicted_class_id = outputs.logits.argmax().item() |
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# Convert class ID to label |
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predicted_label = model.config.id2label[predicted_class_id] |
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print(f"Predicted label: {predicted_label}") |
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``` |
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## Model Card |
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### Model Details |
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- **Model Name**: ModernBERT IMDb Sentiment Analysis |
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- **Base Model**: answerdotai/ModernBERT-base |
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- **Task**: Sentiment Analysis |
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- **Dataset**: IMDb Movie Reviews |
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- **Training Epochs**: 5 |
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### Model Performance |
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- **Test Accuracy**: 95.75% |
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- **Test F1 Score**: 95.75% |
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### Model Architecture |
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- **Base Model**: answerdotai/ModernBERT-base |
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- **Task-Specific Head**: ClassifierHead (from `classifiers.py`) |
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- **Number of Labels**: 2 (Positive, Negative) |
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### Model Inference |
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- **Input Format**: Text (single review) |
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- **Output Format**: Predicted sentiment label (Positive or Negative) |
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### Model Version |
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- **Version**: 1.0 |
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- **Date**: 2025-05-07 |
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### Model License |
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- **License**: MIT License |
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### Model Contact |
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- **Contact**: alocalminima@gmail.com |
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### Model Citation |
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- **Citation**: voxmenthe/modernbert-imdb-sentiment |
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## IMDb Sentiment Analyzer - Gradio App |
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This repository contains a Gradio application for sentiment analysis of IMDb movie reviews. |
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It uses a fine-tuned ModernBERT model hosted on Hugging Face. |
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**Space Link:** [voxmenthe/imdb-sentiment-demo](https://huggingface.co/spaces/voxmenthe/imdb-sentiment-demo) |
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**Model Link:** [voxmenthe/modernbert-imdb-sentiment](https://huggingface.co/voxmenthe/modernbert-imdb-sentiment) |
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## Features |
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* **Text Input**: Analyze custom movie review text. |
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* **Random IMDb Sample**: Load a random review from the IMDb test dataset. |
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* **Sentiment Prediction**: Classifies sentiment as Positive or Negative. |
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* **True Label Display**: Shows the actual IMDb label for loaded samples. |
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## Setup & Running Locally |
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1. **Clone the repository (or your Space repository):** |
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```bash |
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git clone https://huggingface.co/spaces/voxmenthe/imdb-sentiment-demo |
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cd imdb-sentiment-demo |
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``` |
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2. **Install dependencies:** |
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Ensure you have Python 3.11+ installed. |
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```bash |
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pip install -r requirements.txt |
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``` |
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3. **Run the application:** |
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```bash |
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python app.py |
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``` |
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The application will be available at `http://127.0.0.1:7860`. |
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## Model Information |
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The sentiment analysis model is a `ModernBERT` architecture fine-tuned on the IMDb dataset. The specific checkpoint used is `mean_epoch5_0.9575acc_0.9575f1.pt` before being uploaded to `voxmenthe/modernbert-imdb-sentiment`. |
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