King-8/help-classifier

help_classifier

This model is a fine-tuned version of distilbert-base-uncased on the "King-8/help-request-messages" dataset. It achieves the following results on the evaluation set:

• Loss: 1.3083

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🤖 CIC Help Classifier Model

Overview

This model is a fine-tuned text classification model designed to identify the type of help a user needs within the Coding in Color (CIC) ecosystem.

It enables AI systems to understand user challenges and provide structured support.

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🧠 Model Details

• Base model: distilbert-base-uncased
• Task: Text classification
• Training data: CIC Help Classification Dataset
• Framework: Hugging Face Transformers

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📊 Labels

• learning_help
• project_help
• attendance_issue
• technical_issue
• general_guidance

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⚙️ Training

• Epochs: 3
• Dataset size: 100 samples
• Train/Validation/Test split used

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📈 Performance Notes

• Training and validation loss decreased across epochs
• Model performs well on common help scenarios
• Accuracy is limited due to small dataset size

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🧪 Example Usage

predict("I'm stuck on my project and don't know what to do")

Output:

{
  "type": "project_help",
  "confidence": 0.82
}

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🔗 Use Case

This model is designed to be integrated into:

• MCP server tools
• Slack-based support systems
• AI assistants for CIC students

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🚀 Future Improvements

• Fine-tune on larger CIC dataset
• Add real-time feedback learning
• Integrate with response generation models
• Improve classification accuracy with more edge cases

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Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3

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Training results

Training Loss	Epoch	Step	Validation Loss
1.3887	1.0	9	1.4495
1.2613	2.0	18	1.3350
1.1704	3.0	27	1.3083

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Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cpu
• Datasets 4.0.0
• Tokenizers 0.22.2