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---
title: Sentiment Model Comparison
emoji: 🚀
colorFrom: pink
colorTo: indigo
sdk: streamlit
sdk_version: 5.37.0
app_file: app.py
pinned: false
license: mit
short_description: Compare sentiment predictions from two deep learning models
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
# 📊 Sentiment Model Comparison App
This Streamlit app compares two sentiment classification models trained on IMDB movie reviews.
- Model A: 6M params, 50k vocab (fast & lightweight)
- Model B: 34M params, 256k vocab (high capacity)
- Ensemble: Average of both predictions
🔗 **Live Demo:** [Try it on Spaces](https://huggingface.co/spaces/Daksh0505/sentiment-model-comparison)
---
## 🔍 Features
- Enter single review text or upload a CSV (`review` column)
- Get predictions from both models + ensemble average
- Compare probabilities visually
- Submit feedback (saved to Google Sheets)
## 🧠 Models
### 🔹 Model A
- Filename: `sentiment_model_imdb_6.6M.keras`
- **Trainable Parameters**: ~6.6 million
- **Total Parameters**: ~13.06 million
- **Vocabulary Size**: 50,000 tokens
- Description: Lightweight and efficient; optimized for speed.
### 🔹 Model B
- Filename: `sentiment_model_imdb_34M.keras`
- **Trainable Parameters**: ~34 million
- **Total Parameters**: ~99.43 million
- **Vocabulary Size**: 256,000 tokens
- Description: Larger and more expressive; higher accuracy on nuanced reviews.
---
## 🗂 Tokenizers
Each model uses its own tokenizer in Keras JSON format:
- `tokenizer_50k.json` → used with Model A
- `tokenizer_256k.json` → used with Model B
---
## 🔧 Load Models & Tokenizers (from Hugging Face Hub)
```python
from huggingface_hub import hf_hub_download
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.text import tokenizer_from_json
import json
# === Model A ===
model_path_a = hf_hub_download(repo_id="Daksh0505/sentiment-model-imdb", filename="sentiment_model_imdb_6.6M.keras")
tokenizer_path_a = hf_hub_download(repo_id="Daksh0505/sentiment-model-imdb", filename="tokenizer_50k.json")
with open(tokenizer_path_a, "r") as f:
tokenizer_a = tokenizer_from_json(json.load(f))
model_a = load_model(model_path_a)
# === Model B ===
model_path_b = hf_hub_download(repo_id="Daksh0505/sentiment-model-imdb", filename="sentiment_model_imdb_34M.keras")
tokenizer_path_b = hf_hub_download(repo_id="Daksh0505/sentiment-model-imdb", filename="tokenizer_256k.json")
with open(tokenizer_path_b, "r") as f:
tokenizer_b = tokenizer_from_json(json.load(f))
model_b = load_model(model_path_b)
```
---
## 📁 Dataset
- **Source:** [IMDB Multi-Movie Dataset](https://huggingface.co/datasets/Daksh0505/IMDB-Reviews)
## Citation (Please add if you use this dataset)
```ruby
@misc{imdb-multimovie-reviews,
title = {IMDb Multi-Movie Review Dataset},
author = {Daksh Bhardwaj},
year = {2025},
url = {https://huggingface.co/datasets/Daksh0505/IMDB-Reviews
note = {Accessed: 2025-07-17}
}
```
--- |