metadata
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: help_classifier
results: []
datasets:
- King-8/help-request-messages
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
🤖 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.
🧠 Model Details
- Base model:
distilbert-base-uncased - Task: Text classification
- Training data: CIC Help Classification Dataset
- Framework: Hugging Face Transformers
📊 Labels
- learning_help
- project_help
- attendance_issue
- technical_issue
- general_guidance
⚙️ Training
- Epochs: 3
- Dataset size: 100 samples
- Train/Validation/Test split used
📈 Performance Notes
- Training and validation loss decreased across epochs
- Model performs well on common help scenarios
- Accuracy is limited due to small dataset size
🧪 Example Usage
predict("I'm stuck on my project and don't know what to do")
Output:
{
"type": "project_help",
"confidence": 0.82
}
🔗 Use Case
This model is designed to be integrated into:
- MCP server tools
- Slack-based support systems
- AI assistants for CIC students
🚀 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
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
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 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cpu
- Datasets 4.0.0
- Tokenizers 0.22.2