---
language: en
tags:
- text-classification
- osmosis
- education
- coverage-classification
datasets:
- custom
metrics:
- accuracy
- f1
base_model: distilbert-base-uncased
---

# Osmosis Coverage Classifier

This model classifies educational content coverage into three categories: low, fair, and satisfactory.

## Model Description

- **Base Model**: distilbert-base-uncased
- **Task**: Multi-class text classification
- **Classes**: low (0), fair (1), satisfactory (2)
- **Training Data**: Custom osmosis dataset

## Performance

- **Overall Test Accuracy**: 0.7600
- **Macro F1 Score**: 0.7584
- **Macro Precision**: 0.7571
- **Macro Recall**: 0.7600

### Per-Class Performance
- **Low**: Precision: 0.8897, Recall: 0.9240, F1: 0.9065
- **Fair**: Precision: 0.6577, Recall: 0.6365, F1: 0.6469
- **Satisfactory**: Precision: 0.7239, Recall: 0.7193, F1: 0.7216

## Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("KingTechnician/bert-osmosis-coverage-v2")
model = AutoModelForSequenceClassification.from_pretrained("KingTechnician/bert-osmosis-coverage-v2")

# Example usage
objective = "Your learning objective here"
response = "The response text to classify"

inputs = tokenizer(objective, response, return_tensors="pt", padding=True, truncation=True)
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class = torch.argmax(predictions, dim=-1).item()
    
# Map prediction to label
id2label = {0: "low", 1: "fair", 2: "satisfactory"}
predicted_label = id2label[predicted_class]
print(f"Predicted coverage: {predicted_label}")
```

## Training Details

- **Learning Rate**: 2e-5
- **Batch Size**: 64
- **Epochs**: 3
- **Weight Decay**: 0.01