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ML Classifier Training Script (TO BE IMPLEMENTED)
This would train a RoBERTa model on GSM8K dataset
"""
import torch
from transformers import RobertaTokenizer, RobertaForSequenceClassification
from torch.utils.data import DataLoader
import json
# TODO: Implement this for full research version
class MathErrorClassifier:
def __init__(self):
self.model_name = "roberta-base"
self.tokenizer = RobertaTokenizer.from_pretrained(self.model_name)
self.model = RobertaForSequenceClassification.from_pretrained(
self.model_name,
num_labels=2 # VALID or ERROR
)
def prepare_gsm8k_data(self):
"""
Download and prepare GSM8K dataset
https://github.com/openai/grade-school-math
"""
# TODO: Implementation
pass
def train(self, train_data, epochs=3):
"""
Train the classifier
"""
# TODO: Implementation
pass
def evaluate(self, test_data):
"""
Evaluate on test set
"""
# TODO: Implementation
pass
def save_model(self, path):
"""
Save trained model
"""
self.model.save_pretrained(path)
self.tokenizer.save_pretrained(path)
if __name__ == "__main__":
print("⚠️ ML Training Script - Not Yet Implemented")
print("This would require:")
print("1. GSM8K dataset download")
print("2. GPU for training")
print("3. 1-2 weeks training time")
print("\nCurrent system uses simulation for demo purposes.")
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