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sentiment-analysis-ai
TerryPotato/sentiment-analysis-ai
sentiment-analysis-ai is a machine learning model from TerryPotato. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
sentiment-analysis/ │ ├── 📓 1datasetanalysis.ipynb Exploratory data analysis ├── 📓 2baselineevaluation.ipynb Zero-shot FLAN-T5 evaluation ├── 📓 3finetuning.ipynb Model fine-tuning ├── 📓 4posttuningevaluation.ipynb…
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At a glance
- Model type
- t5
- Access
- Public
- Created
- Apr 29, 2026
- Updated
- Apr 29, 2026
- SHA
- 15a3dd48
Model card
From the Hugging Face model README
# 🎬 Movie Review Sentiment Analyzer
> Fine-tuned FLAN-T5 for binary sentiment classification on movie reviews.
---
## 📋 Overview
This project fine-tunes Google's **FLAN-T5 Base** model on the Stanford IMDB dataset to classify movie reviews as **positive** or **negative**. It includes a full ML pipeline from dataset analysis to a deployed interactive web application.
| | Baseline | Fine-tuned | Improvement |
|---|---|---|---|
| **Accuracy** | 93.50% | **96.00%** | +2.50% ✅ |
| **F1 Score** | 0.9372 | **0.9596** | +0.0224 ✅ |
---
## 🗂️ Project Structure
sentiment-analysis/ │ ├── 📓 1_dataset_analysis.ipynb # Exploratory data analysis ├── 📓 2_baseline_evaluation.ipynb # Zero-shot FLAN-T5 evaluation ├── 📓 3_finetuning.ipynb # Model fine-tuning ├── 📓 4_posttuning_evaluation.ipynb # Post-tuning evaluation & comparison ├── 📓 5_app.ipynb # Dashboard HTML generation ├── 🐍 server.py # FastAPI inference server ├── 🌐 dashboard.html # Interactive web dashboard ├── 📊 baseline_metrics.json # Baseline results ├── 📊 finetuned_metrics.json # Fine-tuned results └── 🖼️ *.png # Generated charts
---
## 🚀 Quick Start
### 1. Clone the repository
```bash
git clone https://github.com/TerryPotato/clasificador-rese-as-ia.git
cd sentiment-analysis
2. Create and activate virtual environment
python -m venv sentiment_env
sentiment_env\Scripts\activate # Windows
source sentiment_env/bin/activate # Mac/Linux
3. Install dependencies
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
pip install transformers datasets evaluate accelerate scikit-learn
pip install pandas matplotlib seaborn jupyter
pip install fastapi uvicorn python-multipart
4. Download the fine-tuned model
The model is hosted on HuggingFace Hub. Download it by running this in Python:
from huggingface_hub import snapshot_download
snapshot_download(repo_id="TerryPotato/sentiment-analysis-ai", local_dir="./flan-t5-sentiment-model")
5. Start the server
uvicorn server:app --host 0.0.0.0 --port 8000
6. Open the dashboard
Open dashboard.html in your browser. The green dot confirms the server is online ✅
🧠 Model Details
| Feature | Value |
|---|---|
| Base Model | google/flan-t5-base |
| Parameters | 250M |
| Task | Binary Sentiment Classification |
| Input | Movie review text (English) |
| Output | "positive" or "negative" |
| Max Input Length | 512 tokens |
🔧 Fine-tuning Techniques
| Technique | Purpose |
|---|---|
| Early Stopping (patience=2) | Stops training when Validation Loss stops improving, prevents overfitting |
| Cosine LR Scheduler | Gradually reduces learning rate for more stable convergence |
| Gradient Clipping (max_norm=1.0) | Prevents exploding gradients during backpropagation |
| BF16 Precision | Faster training on modern GPUs with numerical stability |
📊 Training Results
| Epoch | Training Loss | Validation Loss | Status |
|---|---|---|---|
| 1 | 0.1694 | 0.2474 | Decreasing |
| 2 | 0.0632 | 0.1743 | Decreasing |
| 3 | 0.0480 | 0.1450 | ⭐ Best Model |
| 4 | 0.0256 | 0.1639 | Overfitting |
| 5 | 0.0261 | 0.1924 | 🛑 Early Stop |
📦 Dataset
| Feature | Value |
|---|---|
| Name | Stanford IMDB Large Movie Review Dataset |
| Source | stanfordnlp/imdb on HuggingFace |
| Total Reviews | 50,000 |
| Class Balance | 50% positive / 50% negative |
| Language | English |
| Used for Fine-tuning | 2,000 reviews (balanced) |
💻 Hardware Used
| Component | Spec |
|---|---|
| GPU | NVIDIA RTX 5060 Ti |
| CPU | AMD Ryzen 5 9600X |
| RAM | 32 GB |
| Training Time | ~15 minutes |
📁 Notebooks Guide
| Notebook | Description |
|---|---|
1_dataset_analysis.ipynb | Load IMDB dataset, visualize class distribution, word frequency, review lengths |
2_baseline_evaluation.ipynb | Evaluate FLAN-T5 without fine-tuning on 200 balanced reviews |
3_finetuning.ipynb | Fine-tune with Early Stopping, Cosine LR, Gradient Clipping |
4_posttuning_evaluation.ipynb | Compare baseline vs fine-tuned metrics with charts |
5_app.ipynb | Generate the interactive HTML dashboard |
🖥️ Application
The web dashboard includes 4 tabs:
- 📝 Analyze — Submit any review and get real-time sentiment prediction from the model
- 📊 Metrics — Baseline vs Fine-tuned comparison with charts and technique descriptions
- 📈 Training — Epoch-by-epoch loss table and training curve visualization
- 🔍 Dataset — IMDB dataset statistics and exploratory analysis charts
📚 References
- Maas et al. (2011). Learning word vectors for sentiment analysis. ACL.
- Chung et al. (2022). Scaling instruction-finetuned language models. arXiv:2210.11416.
- Wolf et al. (2020). Transformers: State-of-the-art NLP. EMNLP.
---
- Type
- t5
- Created
- Apr 29, 2026
- Updated
- Apr 29, 2026