---
library_name: transformers
license: apache-2.0
tags:
- text-classification
- sequence-classification
- safety
- child-protection
- harmful-content-detection

model-index:
- name: My Awesome Model
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      type: custom
      name: Child Protection Dataset
    metrics:
    - type: accuracy
      value: 0.87
    - type: precision
      value: 0.85
    - type: recall
      value: 0.84
    - type: f1
      value: 0.84
    - type: auc
      value: 0.90

---
# My Awesome Model 🚀
_A fine-tuned BERT model for harmful vs safe child-protection text classification._

## Model Description
This model is a fine-tuned [🤗 Transformers](https://huggingface.co/transformers/) model for **text classification**.  
It was trained to classify text into predefined categories, making it useful for applications such as content moderation, sentiment analysis, or other natural language understanding tasks.  

- **Developed by:** Erin Clarke  
- **Shared by:** Erin Clarke  
- **Model type:** Text Classification (Sequence Classification)  
- **Language(s):** English  
- **License:** Apache 2.0  
- **Finetuned from model:** [e.g., `bert-base-uncased`, `distilroberta-base`]  

---

## Intended Uses
This model is intended for research and practical use in classifying text into categories. Example use cases include:  
- Detecting harmful or safe content  
- Sentiment classification  
- Intent recognition  

**Not for:** production deployment in safety-critical environments without thorough evaluation.  

---

## Training Data
- Dataset: Custom dataset of online chat and text messages labeled for **child protection**, with categories: `harmful` vs `safe`.  
- Size: 12,000 examples total  
- Split: 9,600 train / 1,200 validation / 1,200 test  
- Source: Collected from publicly available, de-identified text datasets and synthetic examples designed for safety research.  
- Preprocessing: Text was cleaned by removing personally identifiable information (PII), lowercasing, and normalizing special characters.  

---

## Training Procedure
- Framework: 🤗 Transformers (Trainer API)  
- Optimizer: AdamW  
- Batch size: 16  
- Learning rate: 5e-5  
- Epochs: 3  
- Hardware: 1× NVIDIA Tesla T4 GPU (Google Colab environment)  
- Loss Function: CrossEntropyLoss (binary classification)  
- Early Stopping: Not used (fixed epochs)  
- Gradient Clipping: Applied at 1.0 to prevent exploding gradients  
- Mixed Precision: FP16 training enabled for efficiency  

---

## Evaluation Results
The model was evaluated on the held-out **test set** of 1,200 examples.  
Metrics are reported for the binary classification task (`harmful` vs `safe`).  

| Metric       | Score |
|--------------|-------|
| Accuracy     | 0.87  |
| Precision    | 0.85  |
| Recall       | 0.84  |
| F1 Score     | 0.84  |
| ROC AUC      | 0.90  |

- **Precision** indicates the proportion of predicted harmful messages that were truly harmful.  
- **Recall** indicates the proportion of actual harmful messages that the model correctly identified.  
- **F1 Score** balances precision and recall for overall effectiveness.  
- **ROC AUC** shows strong discriminatory ability between harmful and safe text.  

---

## Limitations & Bias
- **Bias in Data**: The dataset may reflect biases from the sources used. Certain slang, cultural contexts, or dialects may be underrepresented, which can affect model performance for specific groups of users.  
- **False Negatives**: The model may occasionally classify harmful text as safe, especially if the harmful content is subtle, coded, or context-dependent.  
- **False Positives**: Harmless text may sometimes be flagged as harmful, particularly if it contains strong language used in a non-threatening context.  
- **Generalization**: The model has only been tested on the training and test datasets provided. Performance on live, real-world child protection data may differ.  
- **Sensitive Use Case**: This model is not a replacement for human review. It should only be used as an assistive tool and not as the sole decision-maker in child safety contexts.  
- **Ethical Note**: Any deployment should include monitoring, continuous evaluation, and safeguards to prevent misuse.  

---

---

## How to Use
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("MangoScooter/my-awesome-model")
model = AutoModelForSequenceClassification.from_pretrained("MangoScooter/my-awesome-model")

inputs = tokenizer("Your text here", return_tensors="pt")
outputs = model(**inputs)
```

---

## Citation
If you use this model, please cite:
```
@misc{my-awesome-model,
  author = {Erin Clarke},
  title = {My Awesome Model},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/MangoScooter/my-awesome-model}}
}
```