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| title: Emotion Classifier | |
| emoji: 🎭 | |
| colorFrom: indigo | |
| colorTo: blue | |
| sdk: streamlit | |
| sdk_version: 1.51.0 | |
| app_file: app.py | |
| pinned: false | |
| # 🎭 Emotion Classifier — RoBERTa Based | |
| A deep-learning model that classifies text into **five emotion categories**: | |
| **anger, fear, joy, sadness, surprise**. | |
| Built with **PyTorch, RoBERTa transformer, Streamlit UI** and deployed on **Hugging Face Spaces**. | |
| --- | |
| ## 🧠 Model Details | |
| | Component | Description | | |
| |-----------|-------------| | |
| | Base model | `roberta-base` | | |
| | Task | Single-label Emotion Classification | | |
| | Input | Raw text | | |
| | Output | Softmax probability distribution (5 emotions) | | |
| | Framework | PyTorch + Transformers | | |
| ### Load Model from Hugging Face | |
| ```python | |
| from huggingface_hub import hf_hub_download | |
| import torch | |
| from BertEmotionClassifier import BertEmotionClassifier | |
| model_path = hf_hub_download(repo_id="aadhi3/RoBert_Model", filename="model.pth") | |
| from transformers import AutoTokenizer | |
| tokenizer = AutoTokenizer.from_pretrained("roberta-base") | |
| model = BertEmotionClassifier(model_name="roberta-base", num_labels=5) | |
| state_dict = torch.load(model_path, map_location="cpu") | |
| model.load_state_dict(state_dict) | |
| model.eval() | |
| ``` |