KingTechnician
/

bert-osmosis-coverage-v2

@@ -1,199 +1,71 @@
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 ---
+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