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--- |
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tags: |
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- text-to-image |
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- lora |
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- diffusers |
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- template:diffusion-lora |
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widget: |
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- output: |
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url: images/Screenshot from 2025-08-20 00-50-33.png |
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text: None |
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parameters: |
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negative_prompt: None |
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base_model: ProsusAI/finbert |
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instance_prompt: null |
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license: other |
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license_name: useless |
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license_link: LICENSE |
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--- |
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# Emotion |
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<Gallery /> |
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## Model description |
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Emotion Recognition Model (BERT-based) |
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📌 Overview |
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This is a BERT-based emotion recognition model that I created purely for educational and learning purposes. |
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The model was trained as part of my journey to understand transformers, distillation, GPU management, fine-tuning, and Hugging Face workflows. |
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⚙️ How I built it |
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I started with a pretrained BERT model. |
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I experimented with layer distillation (copying a few layers into a smaller student model). |
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I trained it on an emotion classification dataset to predict different emotional states from text. |
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I focused on hands-on practice: learning about tokenization, GPU memory issues, checkpointing, and model saving/loading. |
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⚠️ Disclaimer |
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This model is not production-ready. |
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It is not optimized for real-world use. |
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It should not be used for commercial, fine-tuning, or deployment purposes. |
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It was built only as a learning exercise to explore Hugging Face and model training. |
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💡 Purpose |
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To help me (and maybe others) understand how Hugging Face works. |
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To practice model distillation and fine-tuning techniques. |
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To learn the workflow of pushing models to Hugging Face Hub. |
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🚫 Limitations |
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Accuracy and reliability are not guaranteed. |
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Not suitable for critical applications (mental health, customer service, etc.). |
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Limited number of layers and trained on a small dataset. |
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## Download model |
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[Download](/Abdullah6395/Text_Emotion_Recognition/tree/main) them in the Files & versions tab. |
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