Update README.md

README.md

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 ---
-license: apache-2.0
 language:
 - en
-base_model:
-- Qwen/Qwen3-8B
-pipeline_tag: text-classification
-library_name: transformers
+license: apache-2.0
+base_model: Qwen/Qwen3-8B
 tags:
-- Math Misconception Annotation
----
+- text-classification
+- education
+- math
+- misconception-detection
+- student-learning
+metrics:
+- map@3
+model-index:
+- name: Qwen3-8B-Math-Misconception-Classifier
+  results:
+  - task:
+      type: text-classification
+      name: Math Misconception Classification
+    metrics:
+    - type: map@3
+      value: 0.944
+      name: Mean Average Precision at 3
+---
+
+# Qwen3-8B Math Misconception Classifier
+
+## Model Description
+
+This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) for identifying and classifying student mathematical misconceptions. The model analyzes student explanations of math problems and predicts the specific misconception category they exhibit.
+
+**Model Architecture:** Qwen3-8B (8 billion parameters)  
+**Task:** Multi-class Text Classification (65 misconception classes)  
+**Performance:** MAP@3 Score of 0.944
+
+## Intended Use
+
+### Primary Use Cases
+- Identifying mathematical misconceptions from student explanations
+- Educational assessment and personalized learning
+- Automated feedback systems for math education
+- Research in mathematics education
+
+### Out-of-Scope Use
+- General text classification tasks outside of math education
+- Real-time production systems without human oversight
+- Any application where misclassification could lead to harm
+
+## Training Details
+
+### Training Data
+The model was trained on the MAP Charting Student Math Misunderstandings dataset, which includes:
+- Mathematical questions with multiple choice answers
+- Student explanations for their answer choices
+- Labels indicating whether the answer was correct
+- Misconception categories and specific misconceptions
+
+### Training Procedure
+
+**Input Format:**
+```
+Question: {QuestionText}
+Answer: {MC_Answer}
+Is Correct Answer: {Yes/No}
+Student Explanation: {StudentExplanation}
+```
+
+This structure provides the model with full context: the question, the student's answer choice, whether it's correct, and their reasoning.
+
+**Preprocessing Steps:**
+1. Created target labels by combining `Category` and `Misconception` columns
+2. Transformed labels into numerical format using label encoding
+3. Identified correct answers and merged this information into the training data
+
+**Training Configuration:**
+- **Model:** Qwen 3 8B
+- **Method:** Full Fine-tuning
+- **Learning Rate:** 2e-5
+- **Epochs:** 3
+- **Batch Size:** 16
+- **Precision:** Mixed precision (FP16/BF16)
+
+## Model Evaluation
+
+The model was evaluated using the MAP@3 metric on the validation set from the competition, achieving a score of 0.944.
+
+**Evaluation Procedure:**
+- Predictions were generated for the validation set
+- MAP@3 score was calculated based on the competition's evaluation script
+
+## Limitations & Bias
+
+- The model is specifically tuned for the MAP competition dataset and may not generalize to other text classification tasks.
+- There may be biases present in the training data that could affect the model's predictions.
+- Misclassifications could occur, especially in cases of ambiguous or unclear student explanations.
+
+## Acknowledgments
+
+This model was developed as part of the MAP (Misconception Annotation Project) competition on Kaggle. Special thanks to the competition hosts and the Kaggle community for their support and collaboration.
+
+## How to Use This Model
+
+To use this model for predicting math misconceptions:
+
+1. Install the required libraries:
+```bash
+pip install transformers torch
+```
+
+2. Load the model and tokenizer:
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model_name = "Qwen3-8B-Math-Misconception-Classifier"
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+```
+
+3. Prepare your input data in the required format.
+
+4. Make predictions:
+```python
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
+results = classifier(your_input_data)
+```
+
+5. Interprete the results, which will include the predicted misconception categories and their associated probabilities.
+
+## References
+
+- [Hugging Face Transformers Documentation](https://huggingface.co/docs/transformers/index)
+- [Kaggle MAP Competition](https://www.kaggle.com/competitions/map-charting-student-math-misunderstandings)