README.md

metadata

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

---

🔍 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)

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

Citation (Please add if you use this dataset)

@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}
}

---