Turning model.py into MVP for today.

.idea/.name

@@ -0,0 +1 @@
+model.py

model.py

@@ -1,88 +1,21 @@
-import pandas as pd
 import torch
-from sklearn.model_selection import train_test_split
-from transformers import BertTokenizer, BertForSequenceClassification, TrainingArguments, Trainer
+from transformers import BertTokenizer, BertForSequenceClassification
 
-# Read the dataset
-df = pd.read_csv('Training_Essay_Data.csv')  # Make sure the file name is correct
-
-# Splitting the dataset
-train_df, eval_df = train_test_split(df, test_size=0.1)
-
-# Tokenizer
+# Tokenizer and Model Initialization
 tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-
-
-# Tokenize function
-def tokenize_function(examples):
-    return tokenizer(examples["text"], padding="max_length", truncation=True, max_length=512)
-
-
-# Tokenize the dataset
-train_encodings = tokenize_function(train_df)
-eval_encodings = tokenize_function(eval_df)
-
-
-# Essay dataset class
-class EssayDataset(torch.utils.data.Dataset):
-    def __init__(self, encodings, labels):
-        self.encodings = encodings
-        self.labels = labels
-
-    def __getitem__(self, idx):
-        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
-        item['labels'] = torch.tensor(int(self.labels[idx]))
-        return item
-
-    def __len__(self):
-        return len(self.labels)
-
-
-# Dataset preparation
-train_dataset = EssayDataset(train_encodings, train_df['label'].tolist())
-eval_dataset = EssayDataset(eval_encodings, eval_df['label'].tolist())
-
-# Model
 model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
 
-# Training arguments
-training_args = TrainingArguments(
-    output_dir='./results',
-    num_train_epochs=3,
-    per_device_train_batch_size=16,
-    per_device_eval_batch_size=64,
-    warmup_steps=500,
-    weight_decay=0.01,
-    logging_dir='./logs',
-    evaluation_strategy="epoch"
-)
-
-# Trainer
-trainer = Trainer(
-    model=model,
-    args=training_args,
-    train_dataset=train_dataset,
-    eval_dataset=eval_dataset
-)
-
-# Train the model
-trainer.train()
-
-# Save the model
-model.save_pretrained("./saved_model")
-
-# Load the model for prediction
+# Load the model (Assuming it's already trained and saved in "./saved_model")
+# If you don't have a trained model, comment out this line. The code will use the default BERT model
 model = BertForSequenceClassification.from_pretrained("./saved_model")
 
-
-# Predicting
+# Predicting Function
 def predict(text):
     inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt")
     outputs = model(**inputs)
     predictions = torch.argmax(outputs.logits, dim=-1)
     return "AI-generated" if predictions.item() == 1 else "Human-written"
 
-
 # Get user input and predict
 user_input = input("Enter the text you want to classify: ")
-print("Classified as:", predict(user_input))
+print("Classified as:", predict(user_input))