---
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](https://huggingface.co/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

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

Output:

```json
{
  "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