Text_Emotion_Recognition

Emotion

<Gallery />

Model description

Emotion Recognition Model (BERT-based) 📌 Overview

This is a BERT-based emotion recognition model that I created purely for educational and learning purposes. The model was trained as part of my journey to understand transformers, distillation, GPU management, fine-tuning, and Hugging Face workflows.

⚙️ How I built it

I started with a pretrained BERT model.

I experimented with layer distillation (copying a few layers into a smaller student model).

I trained it on an emotion classification dataset to predict different emotional states from text.

I focused on hands-on practice: learning about tokenization, GPU memory issues, checkpointing, and model saving/loading.

⚠️ Disclaimer

This model is not production-ready.

It is not optimized for real-world use.

It should not be used for commercial, fine-tuning, or deployment purposes.

It was built only as a learning exercise to explore Hugging Face and model training.

💡 Purpose

To help me (and maybe others) understand how Hugging Face works.

To practice model distillation and fine-tuning techniques.

To learn the workflow of pushing models to Hugging Face Hub.

🚫 Limitations

Accuracy and reliability are not guaranteed.

Not suitable for critical applications (mental health, customer service, etc.).

Limited number of layers and trained on a small dataset.

Download model

Download them in the Files & versions tab.