Spaces:
Sleeping
Sleeping
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README.md
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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# 📝 Review Summariser GPT - Config 1
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This is a Gradio-powered web application that uses a fine-tuned T5 model to summarize long product reviews into short, helpful summaries — and also predicts the overall sentiment (Positive/Negative) of the review.
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- 🔧 Built with: [🤗 Transformers](https://huggingface.co/transformers), [Gradio](https://gradio.app), [Hugging Face Spaces](https://huggingface.co/spaces)
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- 🧠 Model: Fine-tuned from `t5-small` on a custom review-summary dataset
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- 🔍 Sentiment: Powered by `distilbert-base-uncased-finetuned-sst-2-english`
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---
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## 🚀 Demo
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👉 **Live on Hugging Face Spaces**:
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[🔗 Launch App](https://huggingface.co/spaces/Manish014/ReviewSummariser)
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---
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## 📦 Features
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- ✂️ Summarizes long product reviews (e.g., from Amazon, Flipkart, etc.)
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- 📊 Predicts sentiment using a pre-trained classifier
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- 🖼️ Clean Gradio UI with examples and collapsible "About" section
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- ⚡ Optimized for GPU inference (but also works on CPU)
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---
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## 🧪 Example
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**Input Review:**
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> This product leaks water and smells like burnt plastic. Total waste of money.
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**→ Summary:**
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> Leaks and has a bad odor. Not recommended.
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**→ Sentiment:**
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> NEGATIVE (99.8%)
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---
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## 📁 Files in This Repo
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| File | Description |
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|-------------------|------------------------------------------|
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| `app.py` | Main app file with Gradio Blocks UI |
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| `requirements.txt`| Python dependencies for the Space |
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| `thumbnail.png` | Preview image used on Hugging Face Space |
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| `README.md` | This file |
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---
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## 🧠 Model Details
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- **Base Model:** `t5-small`
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- **Fine-Tuning:** Trained on a 1000-sample custom dataset (`review → summary`)
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- **Epochs:** 5
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- **Batch size:** 4
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- **Learning rate:** 5e-5
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- **Training Env:** Google Colab + Kaggle
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---
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## 📄 License
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This project is licensed under the [MIT License](LICENSE).
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---
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## 🙋♂️ Author
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**Manish Kumar Kondoju**
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🌐 [Hugging Face Profile](https://huggingface.co/Manish014)
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---
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> ⭐ Star this repo if you found it useful — and feel free to fork, extend, or share!
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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