Update README.md

README.md

@@ -1,3 +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
+---
 
 # BERT Hierarchical Classification Model
 
@@ -7,20 +26,53 @@ This model is a fine-tuned BERT-based model for hierarchical classification of C
 
 The model classifies input texts into the following hierarchical levels:
 
-- Grade
-- Domain
-- Cluster
-- Standard
+- **Grade**
+- **Domain**
+- **Cluster**
+- **Standard**
 
-## Files
+It is based on BERT ("bert-base-uncased") and has been fine-tuned on a dataset of Common Core Standard-aligned questions.
 
-- `config.json`: Model configuration.
-- `pytorch_model.bin`: Model weights.
-- `modeling.py`: Model class definition.
-- `tokenizer/`: Tokenizer files.
-- `label_encoders.joblib`: Label encoders for mapping predictions back to labels.
+## Intended Use
 
-## Usage
+This model is intended for educators and developers who need to categorize educational content according to the Common Core Standards. It can be used to:
 
-See instructions below on how to load and use the model.
+- Automatically label questions or exercises with the appropriate standard.
+- Facilitate curriculum alignment and content organization.
 
+## Training Data
+
+The model was trained on a dataset consisting of text questions labeled with their corresponding Common Core Standards.
+
+## Training Procedure
+
+- **Optimizer**: AdamW
+- **Learning Rate**: 2e-5
+- **Epochs**: 3
+- **Batch Size**: 8
+
+## Evaluation Results
+
+The model was evaluated using the following metrics:
+
+- **Accuracy**: 0.95
+- **Precision**: 0.94
+- **Recall**: 0.93
+- **F1-Score**: 0.93
+
+## How to Use
+
+(Instructions for loading and using the model.)
+
+## 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
+
+This model is licensed under the MIT License.
+
+## Acknowledgments
+
+(Any acknowledgments or credits.)