Update README.md

README.md

@@ -1,22 +1,22 @@
----
-language: en
-tags:
-- text-classification
-- hierarchical-classification
-- common-core-standards
-license: mit
-datasets:
-- iolimat482/common-core-math-question-khan-academy-and-mathfish
-metrics:
-- accuracy
-- precision
-- recall
-- f1
-library_name: transformers
-pipeline_tag: text-classification
-base_model:
-- google-bert/bert-base-uncased
----
+---
+language: en
+tags:
+- text-classification
+- hierarchical-classification
+- common-core-standards
+license: mit
+datasets:
+- iolimat482/common-core-math-question-khan-academy-and-mathfish
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+library_name: transformers
+pipeline_tag: text-classification
+base_model:
+- google-bert/bert-base-uncased
+---
 
 # BERT Hierarchical Classification Model
 
@@ -48,31 +48,152 @@ The model was trained on a dataset consisting of text questions labeled with the
 
 - **Optimizer**: AdamW
 - **Learning Rate**: 2e-5
-- **Epochs**: 3
-- **Batch Size**: 8
+- **Epochs**: 10
+- **Batch Size**: 16
 
-## Evaluation Results
+## Evaluation
 
-The model was evaluated using the following metrics:
+The model was evaluated on multiple classification tasks, including cluster classification, domain classification, grade classification, and standard classification. The performance metrics used for evaluation are Accuracy, F1 Score, Precision, and Recall. Below are the results after training for **10 epochs**:
 
-- **Accuracy**: 0.95
-- **Precision**: 0.94
-- **Recall**: 0.93
-- **F1-Score**: 0.93
+### Overall Loss
+
+- **Average Training Loss**: 0.2508
+- **Average Validation Loss**: 1.9785
+- **Training Loss**: 0.1843
+
+### Cluster Classification
+
+| Metric       | Value   |
+|--------------|---------|
+| **Accuracy** | 0.8797  |
+| **F1 Score** | 0.8792  |
+| **Precision**| 0.8840  |
+| **Recall**   | 0.8797  |
+
+### Domain Classification
+
+| Metric       | Value   |
+|--------------|---------|
+| **Accuracy** | 0.9177  |
+| **F1 Score** | 0.9175  |
+| **Precision**| 0.9183  |
+| **Recall**   | 0.9177  |
+
+### Grade Classification
+
+| Metric       | Value   |
+|--------------|---------|
+| **Accuracy** | 0.8858  |
+| **F1 Score** | 0.8861  |
+| **Precision**| 0.8896  |
+| **Recall**   | 0.8858  |
+
+### Standard Classification
+
+| Metric       | Value   |
+|--------------|---------|
+| **Accuracy** | 0.8334  |
+| **F1 Score** | 0.8323  |
+| **Precision**| 0.8433  |
+| **Recall**   | 0.8334  |
 
 ## How to Use
 
-(Instructions for loading and using the model.)
+```python
+import torch
+from transformers import BertTokenizer, BertConfig
+from huggingface_hub import hf_hub_download
+import joblib
+import importlib.util
+
+tokenizer = BertTokenizer.from_pretrained('iolimat482/common-core-bert-hierarchical-classification')
+
+config = BertConfig.from_pretrained('iolimat482/common-core-bert-hierarchical-classification')
+
+# Download 'modeling.py'
+modeling_file = hf_hub_download(repo_id='iolimat482/common-core-bert-hierarchical-classification', filename='modeling.py')
+
+# Load the model class
+spec = importlib.util.spec_from_file_location("modeling", modeling_file)
+modeling = importlib.util.module_from_spec(spec)
+spec.loader.exec_module(modeling)
+
+BertHierarchicalClassification = modeling.BertHierarchicalClassification
+
+# Instantiate the model
+model = BertHierarchicalClassification(config)
+
+# Load model weights
+model_weights = hf_hub_download(repo_id='iolimat482/common-core-bert-hierarchical-classification', filename='best_model.pt')
+model.load_state_dict(torch.load(model_weights, map_location=torch.device('cpu')))
+
+model.eval()
+
+label_encoders_path = hf_hub_download(repo_id='iolimat482/common-core-bert-hierarchical-classification', filename='label_encoders.joblib')
+label_encoders = joblib.load(label_encoders_path)
+
+def predict_standard(model, tokenizer, label_encoders, text):
+    # Tokenize input text
+    inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True)
+
+    # Perform inference
+    with torch.no_grad():
+        grade_logits, domain_logits, cluster_logits, standard_logits = model(inputs['input_ids'], inputs['attention_mask'])
+
+    # Get the predicted class indices
+    grade_pred = torch.argmax(grade_logits, dim=1).item()
+    domain_pred = torch.argmax(domain_logits, dim=1).item()
+    cluster_pred = torch.argmax(cluster_logits, dim=1).item()
+    standard_pred = torch.argmax(standard_logits, dim=1).item()
+
+    # Map indices to labels
+    grade_label = label_encoders['Grade'].inverse_transform([grade_pred])[0]
+    domain_label = label_encoders['Domain'].inverse_transform([domain_pred])[0]
+    cluster_label = label_encoders['Cluster'].inverse_transform([cluster_pred])[0]
+    standard_label = label_encoders['Standard'].inverse_transform([standard_pred])[0]
+
+    return {
+        'Grade': grade_label,
+        'Domain': domain_label,
+        'Cluster': cluster_label,
+        'Standard': standard_label
+    }
+
+# Example questions
+questions = [
+    "Add 4 and 5 together. What is the sum?",
+    "What is 7 times 8?",
+    "Find the area of a rectangle with length 5 and width 3.",
+]
+
+for question in questions:
+    prediction = predict_standard(model, tokenizer, label_encoders, question)
+    print(f"Question: {question}")
+    print("Predicted Standards:")
+    for key, value in prediction.items():
+        print(f"  {key}: {value}")
+    print("\n")
+```
 
 ## Limitations
 
 - The model's performance is limited to the data it was trained on.
 - May not generalize well to questions significantly different from the training data.
 
-## License
+## Citation
 
-This model is licensed under the MIT License.
+If you use this model in your work, please cite:
 
-## Acknowledgments
+```bibtex
+@misc{olaimat2025commoncore,
+    author = {Olaimat, Ibrahim},
+    title = {Common Core BERT Hierarchical Classification},
+    year = {2025},
+    howpublished = {\url{https://huggingface.co/iolimat482/common-core-bert-hierarchical-classification}}
+}
+```
 
-(Any acknowledgments or credits.)
+## Connect with the Author
+- 🤗 Hugging Face: [@iolimat482](https://huggingface.co/iolimat482)
+- 💼 LinkedIn: [Ibrahim Olaimat](https://www.linkedin.com/in/ibrahim-olaimat-8ba1b4211)
+```