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