King-8/help-classifier-v2

🤖 Help Classifier Model (v2)

🧠 Overview

The Help Classifier Model (v2) is a fine-tuned NLP model designed to classify student help requests into meaningful categories within a collaborative learning environment.

This model is part of a larger AI system built for the Coding in Color (CIC) ecosystem, supporting students working across domains such as AI development, game development, 2D/3D art, and robotics.

Its primary purpose is to:

• Interpret real student messages
• Identify intent behind help requests
• Route inputs to appropriate downstream systems (e.g., generators, agents)

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🚀 Version Update (v1 → v2)

🔹 v1

• Trained on ~100 examples
• Limited generalization
• Struggled with messy or informal input

🔹 v2 (Current)

• Trained on 1,000 examples

• Balanced dataset across all categories

• Strong performance on:

  • informal/slang input
  • mixed tone messages
  • ambiguous phrasing
  • real CIC-style check-ins

👉 v2 significantly improves accuracy, stability, and real-world usability

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🧩 Task Definition

Task Type: Text Classification

Input: Student message Output: One of 5 help categories

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🏷️ Labels

Label	Description
learning_help	User is trying to understand a concept or skill
project_help	User needs direction or next steps in a project
technical_issue	Something is broken or not working
attendance_issue	User missed a meeting or needs to catch up
general_guidance	User expresses uncertainty, stress, or needs advice

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🏗️ Model Architecture

• Base Model: distilbert-base-uncased
• Fine-tuned for sequence classification
• Number of labels: 5

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⚙️ Training Configuration

• Epochs: 4
• Learning Rate: 2e-5
• Batch Size: 8
• Weight Decay: 0.01
• Train/Validation Split: 80/10/10

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📊 Training Results

Epoch	Training Loss	Validation Loss
1	0.552	0.512
2	0.111	0.122
3	0.032	0.077
4	0.025	0.064

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📈 Performance Summary

• Low validation loss (~0.06)

• Strong generalization across unseen inputs

• Stable convergence during training

• Handles:

  • messy/slang text
  • indirect requests
  • multi-layered inputs

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🧪 Example Predictions

Input:

i missed the meeting and now idk what we’re doing

Output:

attendance_issue

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Input:

my model works but the predictions are weird and I don’t know why

Output:

technical_issue

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Input:

I feel like I’m behind and don’t know what to focus on

Output:

general_guidance

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🔗 System Integration

This model is integrated into an MCP (Model Context Protocol) system where it acts as:

Entry-point classifier for routing student inputs

Pipeline example:

User Input → Help Classifier → (Future: Generator / Summarizer)

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🎯 Use Cases

• Help request classification
• Slack/Discord message routing
• Educational AI assistants
• CIC ecosystem tools
• AI agent pipelines

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⚠️ Limitations

• Single-label classification (some messages may contain multiple intents)
• Edge cases may still overlap between categories
• Domain-specific (focused on student tech environments)

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🔮 Future Improvements

• Multi-label classification
• Larger dataset (2,000+ examples)
• Confidence scoring
• Integration with response generation models
• Continuous retraining with real user data

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👤 Author

Created by Kingston Lewis as part of the Coding in Color program for the AI Dev team.

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help-classifier-v2

This model is a fine-tuned version of distilbert-base-uncased on the King-8/help-request-messages-v2 dataset. It achieves the following results on the evaluation set:

• Loss: 0.0643

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: 4
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.5524	1.0	88	0.5124
0.1114	2.0	176	0.1221
0.0324	3.0	264	0.0771
0.0249	4.0	352	0.0643

Framework versions

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