To address the class imbalance in your news text data and train a high-accuracy RoBERTa model, you can follow these steps:

1. Data resampling: a. Oversampling: Increase the number of samples for minority classes by duplicating or generating synthetic samples using techniques like SMOTE (Synthetic Minority Over-sampling Technique). b. Undersampling: Reduce the number of samples for majority classes by randomly removing samples or using techniques like Tomek Links or Neighborhood Cleaning Rule. c. Combination: Apply a combination of oversampling and undersampling to balance the dataset.

2. Use stratified sampling: When splitting the dataset into training and validation sets, use stratified sampling to ensure that each class's proportion is maintained in both sets.

3. Apply class weighting: Assign higher weights to the minority classes during model training. This can be done by calculating class weights inversely proportional to the class frequencies or using sklearn's compute_class_weight function. Pass these weights to the loss function or the model training process.

4. Experiment with cost-sensitive learning: Modify the learning process to be more sensitive to misclassifying minority classes by introducing different misclassification costs for each class.

5. Ensemble methods: Use ensemble techniques like bagging or boosting with balanced sampling for better model performance. These methods can improve generalization and stability.

For RoBERTa model-specific hyperparameters and settings, consider the following:

1. Model size: You may choose the appropriate RoBERTa model size (e.g., roberta-base, roberta-large) based on your computational resources and the complexity of your data.

2. Learning rate: Experiment with different learning rates (e.g., 1e-5, 2e-5, 5e-5) to find the one that works best for your task.

3. Batch size: Choose a suitable batch size (e.g., 16, 32) depending on your computational resources. Smaller batch sizes might help the model generalize better.

4. Epochs: Train the model for multiple epochs (e.g., 3-5), but be cautious of overfitting. Use early stopping or monitor validation performance to prevent overfitting.

5. Max sequence length: Select an appropriate maximum sequence length for the text inputs based on the length of the news articles in your dataset.

6. Fine-tuning: Fine-tune the RoBERTa model on your task using the training set.

7. Evaluation metric: Since the dataset is imbalanced, using accuracy as a metric might not be the best choice. Consider using other metrics like F1-score, precision, recall, or the area under the ROC curve (AUC-ROC).

Remember that hyperparameter tuning can be time-consuming and might require multiple experiments. Using tools like Grid Search, Random Search, or Bayesian Optimization can help you find the best hyperparameters for your task more efficiently.

Here's an updated version of your code with the improvements I suggested earlier:

pythonCopy code
import pandas as pd
import torch
import numpy as np
from sklearn.metrics import f1_score
from sklearn.model_selection import train_test_split
from sklearn.utils.class_weight import compute_class_weight
from transformers import RobertaTokenizer, RobertaForSequenceClassification, AdamW, get_linear_schedule_with_warmup

# Check if GPU is available and set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load the training data
train_df = pd.read_csv('train.csv')

# Split the data into training and validation sets using stratified sampling
train_df, val_df = train_test_split(train_df, test_size=0.1, stratify=train_df['label'], random_state=42)

# Initialize the RoBERTa tokenizer and model
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaForSequenceClassification.from_pretrained('roberta-base', num_labels=8).to(device)

# Tokenize the text data and create input tensors for training set
train_inputs = tokenizer(list(train_df['text']), padding=True, truncation=True, max_length=512, return_tensors="pt").to(device)
train_labels = torch.tensor(list(train_df['label'])).to(device)

# Tokenize the text data and create input tensors for validation set
val_inputs = tokenizer(list(val_df['text']), padding=True, truncation=True, max_length=512, return_tensors="pt").to(device)
val_labels = torch.tensor(list(val_df['label'])).to(device)

# Calculate the class weights to handle class imbalance
class_weights = compute_class_weight('balanced', np.unique(train_df['label']), train_df['label'])
class_weights = torch.tensor(class_weights).to(device)

# Fine-tune the RoBERTa model
optimizer = AdamW(model.parameters(), lr=2e-5)
num_epochs = 3
total_steps = len(train_inputs) * num_epochs
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=100, num_training_steps=total_steps)

# Create a function to train and validate the model
def train_and_validate(model, train_inputs, train_labels, val_inputs, val_labels, optimizer, scheduler):
    model.train()
    for epoch in range(num_epochs):
        optimizer.zero_grad()
        outputs = model(**train_inputs, labels=train_labels)
        loss = torch.nn.CrossEntropyLoss(weight=class_weights)(outputs.logits, train_labels)
        loss.backward()
        optimizer.step()
        scheduler.step()

        model.eval()
        with torch.no_grad():
            val_outputs = model(**val_inputs, labels=val_labels)
            val_loss = torch.nn.CrossEntropyLoss(weight=class_weights)(val_outputs.logits, val_labels)
            val_predictions = val_outputs.logits.argmax(dim=-1).tolist()
            val_f1 = f1_score(val_labels.cpu().numpy(), val_predictions, average='weighted')
        print(f'Epoch: {epoch + 1}, Validation Loss: {val_loss.item()}, Validation F1-score: {val_f1}')

train_and_validate(model, train_inputs, train_labels, val_inputs, val_labels, optimizer, scheduler)
print("training finished")

# Load the test data
test_df = pd.read_csv('test.csv')

# Tokenize the test data and create input tensors
test_inputs = tokenizer(list(test_df['text']), padding=True, truncation=True, max_length=512, return_tensors="pt").to(device)

# Make predictions on the test data using the fine-tuned model
model.eval()
with torch.no_grad():
    test_outputs = model(**test_inputs)
    test_predictions

Here's the continuation of your code:

pythonCopy code
# Make predictions on the test data using the fine-tuned model
model.eval()
with torch.no_grad():
    test_outputs = model(**test_inputs)
    test_predictions = test_outputs.logits.argmax(dim=-1).tolist()

# Save the predicted category labels in a CSV file
sample_submission_df = pd.DataFrame({'id': list(test_df['id']), 'label': test_predictions})
sample_submission_df.to_csv('sample_submission.csv', index=False)
print('submission completed.')

Now your code should be complete, including the improvements discussed earlier for handling class imbalance, using stratified sampling, and calculating performance metrics on a validation set. This should help improve your model's performance on the provided news text data.

The slow training speed in your code could be due to several factors, including the large dataset size, long input sequences, or lack of mini-batch processing. Here are some suggestions to speed up the training:

1. Use DataLoader and batch processing: Instead of processing the entire dataset at once, split the data into smaller mini-batches using PyTorch's DataLoader. This allows the model to train on a small subset of data in each iteration, which can improve training speed and memory usage.

2. Reduce the maximum sequence length: By reducing the max_length parameter in the tokenizer, you will limit the number of tokens processed per input. This can speed up training, but be careful not to lose too much information by truncating the input text too much.

3. Gradient accumulation: Accumulate gradients over several mini-batch iterations before updating the model's weights. This can help reduce the memory usage while maintaining the benefits of a larger batch size.

Here's an updated version of your code with these improvements:

pythonCopy code
import pandas as pd
import torch
from torch.utils.data import Dataset, DataLoader
from sklearn.metrics import f1_score
from transformers import RobertaTokenizer, RobertaForSequenceClassification, AdamW, get_linear_schedule_with_warmup

class NewsDataset(Dataset):
    def __init__(self, dataframe, tokenizer, max_length=512):
        self.dataframe = dataframe
        self.tokenizer = tokenizer
        self.max_length = max_length

    def __len__(self):
        return len(self.dataframe)

    def __getitem__(self, idx):
        text = self.dataframe.iloc[idx]['text']
        label = self.dataframe.iloc[idx]['label']
        inputs = self.tokenizer(text, padding="max_length", truncation=True, max_length=self.max_length, return_tensors="pt")
        inputs["input_ids"] = inputs["input_ids"].squeeze()
        inputs["attention_mask"] = inputs["attention_mask"].squeeze()
        return {"inputs": inputs, "label": label}

# Check if GPU is available and set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load the training data
train_df = pd.read_csv('train.csv')

# Initialize the RoBERTa tokenizer and model
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaForSequenceClassification.from_pretrained('roberta-base', num_labels=8).to(device)

# Create a DataLoader for the training data
train_dataset = NewsDataset(train_df, tokenizer)
train_dataloader = DataLoader(train_dataset, batch_size=8, shuffle=True)

# Calculate the class weights to handle class imbalance
class_weights = {}
total = sum([14146, 10961, 9379, 8946, 2461, 1022, 278, 206])
for i in range(8):
    class_weights[i] = total / (8 * [14146, 10961, 9379, 8946, 2461, 1022, 278, 206][i])
class_weights = torch.tensor(list(class_weights.values())).to(device)

# Fine-tune the RoBERTa model
optimizer = AdamW(model.parameters(), lr=2e-5)
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=100, num_training_steps=1000)
model.train()

epochs = 3
accumulation_steps = 4

for epoch in range(epochs):
    epoch_loss = 0.0
    optimizer.zero_grad()
    for i, batch in enumerate(train_dataloader):
        inputs = {key: value.to(device) for key, value in batch["inputs"].items()}
        labels

My apologies for the abrupt ending. Here's the continuation of the code:

pythonCopy code
        labels = batch["label"].to(device)

        # Forward pass
        outputs = model(**inputs, labels=labels)
        loss = torch.nn.CrossEntropyLoss(weight=class_weights)(outputs.logits, labels)

        # Backward pass
        loss.backward()

        # Gradient accumulation
        if (i+1) % accumulation_steps == 0:
            optimizer.step()
            scheduler.step()
            optimizer.zero_grad()

        epoch_loss += loss.item()

    epoch_loss /= len(train_dataloader)
    print(f"Epoch {epoch+1} Loss: {epoch_loss}")

print("Training finished.")

# Load the test data
test_df = pd.read_csv('test.csv')

# Create a DataLoader for the test data
test_dataset = NewsDataset(test_df, tokenizer)
test_dataloader = DataLoader(test_dataset, batch_size=8, shuffle=False)

# Make predictions on the test data using the fine-tuned model
model.eval()
test_predictions = []

with torch.no_grad():
    for batch in test_dataloader:
        inputs = {key: value.to(device) for key, value in batch["inputs"].items()}

        # Forward pass
        outputs = model(**inputs)
        predictions = outputs.logits.argmax(dim=-1).tolist()
        test_predictions.extend(predictions)

# Save the predicted category labels in a CSV file
sample_submission_df = pd.DataFrame({'id': list(test_df['id']), 'label': test_predictions})
sample_submission_df.to_csv('sample_submission.csv', index=False)
print('

You are correct. In the code above, the test_df should not have a label column, as it is used for making predictions on unseen data. We should update the NewsDataset class to account for this. Here's the updated version:

pythonCopy code
class NewsDataset(Dataset):
    def __init__(self, dataframe, tokenizer, max_length=512, has_labels=True):
        self.dataframe = dataframe
        self.tokenizer = tokenizer
        self.max_length = max_length
        self.has_labels = has_labels

    def __len__(self):
        return len(self.dataframe)

    def __getitem__(self, idx):
        text = self.dataframe.iloc[idx]['text']
        inputs = self.tokenizer(text, padding="max_length", truncation=True, max_length=self.max_length, return_tensors="pt")
        inputs["input_ids"] = inputs["input_ids"].squeeze()
        inputs["attention_mask"] = inputs["attention_mask"].squeeze()

        if self.has_labels:
            label = self.dataframe.iloc[idx]['label']
            return {"inputs": inputs, "label": label}
        else:
            return {"inputs": inputs}

Now, when you create a DataLoader for the test data, you can set the has_labels parameter to False:

pythonCopy code
# Create a DataLoader for the test data
test_dataset = NewsDataset(test_df, tokenizer, has_labels=False)
test_dataloader = DataLoader(test_dataset, batch_size=8, shuffle=False)

This updated version of the NewsDataset class will handle

The provided code looks good overall. However, there are a few potential improvements and optimizations:

1. DataLoader num_workers: You can increase the number of workers in the DataLoader to speed up the data loading process by parallelizing the work. This can be particularly useful when working with large datasets. Set num_workers to the number of available CPU cores minus one (to leave one core for other tasks):

pythonCopy code
train_dataloader = DataLoader(train_dataset, batch_size=8, shuffle=True, num_workers=torch.get_num_threads() - 1)
test_dataloader = DataLoader(test_dataset, batch_size=8, shuffle=False, num_workers=torch.get_num_threads() - 1)

2. Save the model periodically: It's a good practice to save your model checkpoints periodically during training. This can help you recover your model if something unexpected happens or if you want to revert to a previous model state.

pythonCopy code
save_checkpoint_path = "checkpoint_epoch_{}.pt"

for epoch in range(epochs):
    # Training code ...
    
    # Save the model checkpoint after each epoch
    torch.save(model.state_dict(), save_checkpoint_path.format(epoch + 1))

3. Use a validation set: To get a better understanding of your model's performance during training, consider adding a validation set. This can help you monitor overfitting and decide when to stop training. Split the training data into a train and validation set and create DataLoaders for both. At the end of each epoch, evaluate the model on the validation set and print the loss and/or other

Applying k-fold cross-validation to the code above can potentially produce better results by providing a more robust evaluation of the model's performance. In k-fold cross-validation, the training data is divided into k subsets (or "folds"). The model is then trained k times, each time using k-1 folds for training and one fold for validation. This way, every sample in the dataset is used once for validation. The final model performance is calculated as the average performance across all k folds.

To apply k-fold cross-validation to the code above, you can use the following approach:

1. Import the required library:

pythonCopy code
from sklearn.model_selection import StratifiedKFold

2. Create the k-fold splitter:

pythonCopy code
n_splits = 5
kfold = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=42)

3. Modify the training loop to perform k-fold cross-validation:

pythonCopy code
for fold, (train_idx, val_idx) in enumerate(kfold.split(train_df, train_df['label'])):
    print(f'Fold {fold + 1}')
    
    # Split the data into train and validation sets
    train_fold_df = train_df.iloc[train_idx]
    val_fold_df = train_df.iloc[val_idx]
    
    # Create DataLoaders for the train and validation sets
    train_fold_dataset = NewsDataset(train_fold_df, tokenizer)
    train_fold_dataloader = DataLoader(train_fold_dataset, batch_size=8, shuffle=True, num_workers=torch.get_num_threads() - 1)
    
    val_fold_dataset = NewsDataset(val_fold_df, tokenizer)
    val_fold_dataloader = DataLoader(val_fold_dataset, batch_size=8, shuffle=False, num_workers=torch.get_num_threads()

Certainly! Continuing from the previous response, here is the rest of the code for applying k-fold cross-validation:

pythonCopy code
    - 1)

    # Initialize a new model for the current fold
    model = RobertaForSequenceClassification.from_pretrained('roberta-base', num_labels=8).to(device)

    # Fine-tune the RoBERTa model
    optimizer = AdamW(model.parameters(), lr=2e-5)
    scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=100, num_training_steps=1000)
    model.train()

    for epoch in range(epochs):
        print(f'Fold {fold + 1} - Epoch {epoch + 1}')
        epoch_loss = 0.0
        optimizer.zero_grad()
        for i, batch in enumerate(train_fold_dataloader):
            inputs = {key: value.to(device) for key, value in batch["inputs"].items()}
            labels = batch["label"].to(device)

            # Forward pass
            outputs = model(**inputs, labels=labels)
            loss = torch.nn.CrossEntropyLoss(weight=class_weights)(outputs.logits, labels)

            # Backward pass
            loss.backward()

            # Gradient accumulation
            if (i + 1) % accumulation_steps == 0:
                optimizer.step()
                scheduler.step()
                optimizer.zero_grad()

            epoch_loss += loss.item()

        epoch_loss /= len(train_fold_dataloader)
        print(f"Fold {fold + 1} - Epoch {epoch + 1} Loss: {epoch_loss}")

    print(f"Fold {fold + 

Here's the code incorporating k-fold cross-validation using the provided code:

pythonCopy code
import pandas as pd
import torch
from torch.utils.data import Dataset, DataLoader
from sklearn.metrics import f1_score
from sklearn.model_selection import StratifiedKFold
from transformers import RobertaTokenizer, RobertaForSequenceClassification, AdamW, get_linear_schedule_with_warmup

# ... (Keep the NewsDataset class definition unchanged) ...

# Check if GPU is available and set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load the training data
train_df = pd.read_csv('train.csv')

# Initialize the RoBERTa tokenizer
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')

# Calculate the class weights to handle class imbalance
class_weights = {}
total = sum([14146, 10961, 9379, 8946, 2461, 1022, 278, 206])
for i in range(8):
    class_weights[i] = total / (8 * [14146, 10961, 9379, 8946, 2461, 1022, 278, 206][i])
class_weights = torch.tensor(list(class_weights.values())).to(device)

epochs = 3
accumulation_steps = 4

# Set up k-fold cross-validation
k_folds = 5
kf = StratifiedKFold(n_splits=k_folds, shuffle=True, random_state=42)

# Start k-fold cross-validation
for fold, (train_indices, val_indices) in enumerate(kf.split(train_df, train_df["label"])):

    # Prepare data for the current fold
    train_fold_df = train_df.iloc[train_indices]
    val_fold_df = train_df.iloc[val_indices]

    train_fold_dataset = NewsDataset(train_fold_df, tokenizer)
    val_fold_dataset = NewsDataset(val_fold_df, tokenizer)

    train_fold_dataloader = DataLoader(train_fold_dataset, batch_size=8, shuffle=True)
    val_fold_dataloader = DataLoader(val_fold_dataset, batch_size=8, shuffle=False)

    # Initialize a new model for the current fold
    model = RobertaForSequenceClassification.from_pretrained('roberta-base', num_labels=8).to(device)

    # Fine-tune the RoBERTa model
    optimizer = AdamW(model.parameters(), lr=2e-5)
    scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=100, num_training_steps=1000)
    model.train()

    for epoch in range(epochs):
        print(f'Fold {fold + 1} - Epoch {epoch + 1}')
        epoch_loss = 0.0
        optimizer.zero_grad()
        for i, batch in enumerate(train_fold_dataloader):
            inputs = {key: value.to(device) for key, value in batch["inputs"].items()}
            labels = batch["label"].to(device)

            # Forward pass
            outputs = model(**inputs, labels=labels)
            loss = torch.nn.CrossEntropyLoss(weight=class_weights)(outputs.logits, labels)

            # Backward pass
            loss.backward()

            # Gradient accumulation
            if (i + 1) % accumulation_steps == 0:
                optimizer.step()
                scheduler.step()
                optimizer.zero_grad()

            epoch_loss += loss.item()

        epoch_loss /= len(train_fold_dataloader)
        print(f"Fold {fold + 1} - Epoch {epoch + 1} Loss: {epoch_loss}")

    # Validation step for the current fold
    model.eval()
    val_predictions = []
    val_true_labels = []

    with torch.no_grad():
        for batch in

Here's the continuation of the code incorporating k-fold cross-validation using the provided code:

pythonCopy code
        for batch in val_fold_dataloader:
            inputs = {key: value.to(device) for key, value in batch["inputs"].items()}
            labels = batch["label"].to(device)

            # Forward pass
            outputs = model(**inputs)
            predictions = outputs.logits.argmax(dim=-1).tolist()
            val_predictions.extend(predictions)
            val_true_labels.extend(labels.tolist())

    # Calculate F1 score for the current fold
    fold_f1_score = f1_score(val_true_labels, val_predictions, average='weighted')
    print(f"Fold {fold + 1} F1 Score: {fold_f1_score}")

# Load the test data
test_df = pd.read_csv('test.csv')

# Create a DataLoader for the test data
test_dataset = NewsDataset(test_df, tokenizer, has_labels=False)
test_dataloader = DataLoader(test_dataset, batch_size=8, shuffle=False, num_workers=torch.get_num_threads() - 1)

# Make predictions on the test data using the fine-tuned model (use the last trained model as an example)
model.eval()
test_predictions = []

with torch.no_grad():
    for batch in test_dataloader:
        inputs = {key: value.to(device) for key, value in batch["inputs"].items()}

        # Forward pass
        outputs = model(**inputs)
        predictions = outputs.logits.argmax(dim=-1).tolist()
        test_predictions.extend(predictions)

# Save the predicted category labels in a CSV file
sample_submission_df = pd.DataFrame({'id': list(test_df['id']), 'label': test_predictions})
sample_submission_df.to_csv('submission1.csv', index=False)
print('submission completed')

This code snippet continues the validation step for each fold and calculates the F1 score for the validation set. Finally, it performs predictions on the test dataset using the last trained model as an example and saves the results in a CSV file.

이 에러는 NewsDataset 클래스를 파이썬의 멀티프로세싱 모듈과 함께 사용하려고 할 때 발생합니다. 이 문제를 해결하기 위해 클래스 정의를 별도의 파일로 이동시키고 해당 파일을 import하여 사용할 수 있습니다.

예를 들어, news_dataset.py라는 새 파일을 만들고 NewsDataset 클래스 정의를 그 파일로 이동시키세요:

pythonCopy code
# news_dataset.py

import pandas as pd
from torch.utils.data import Dataset
from transformers import RobertaTokenizer

class NewsDataset(Dataset):
    ...

그런 다음 RoBERTa.py 파일에서 클래스를 import하고 사용합니다.

pythonCopy code
# RoBERTa.py
import pandas as pd
import torch
from torch.utils.data import DataLoader
from sklearn.metrics import f1_score
from sklearn.model_selection import StratifiedKFold
from transformers import RobertaTokenizer, RobertaForSequenceClassification, AdamW, get_linear_schedule_with_warmup

from news_dataset import NewsDataset  # Import the NewsDataset class

# ... (rest of the code) ...

이렇게 하면, 이전에 발생한 PicklingError 문제를 해결할 수 있습니다.

The error you encountered does not directly affect the performance of the model. However, it prevents the code from running properly due to a problem with pickling the NewsDataset class when using multiprocessing. This means that your model will not be trained or evaluated correctly, which indirectly affects its performance.

By following the solution I provided in the previous response, you can fix the error and continue training and evaluating your model properly. Once the error is resolved, it should not have any impact on your model's performance.

Yes, you can solve the error without creating a new file by using a different method for parallelism. One way is to use the torch.multiprocessing library instead of the standard multiprocessing library.

Here's an example of how to modify the DataLoader to use torch.multiprocessing:

1. Import torch.multiprocessing at the beginning of your code:

pythonCopy code
import torch.multiprocessing as mp

2. Set the start method for multiprocessing:

pythonCopy code
if __name__ == '__main__':
    mp.set_start_method('spawn')

3. Replace num_workers=torch.get_num_threads() - 1 with num_workers=mp.cpu_count() - 1 when creating the DataLoaders:

pythonCopy code
train_dataloader = DataLoader(train_dataset, batch_size=8, shuffle=True, num_workers=mp.cpu_count() - 1)
test_dataloader = DataLoader(test_dataset, batch_size=8, shuffle=False, num_workers=mp.cpu_count() - 1)

By using torch.multiprocessing, you should be able to avoid the pickling issue and run your code without needing to create a separate file for the NewsDataset class.

You should put the if __name__ == '__main__': block at the very end of your code, after all other code blocks (e.g., after defining classes, functions, and executing the main logic of your script). The code inside the if __name__ == '__main__': block will only run when the script is executed directly, not when it's imported as a module.

Here's an example of how to structure your code:

pythonCopy code
import pandas as pd
import torch
import torch.multiprocessing as mp
from torch.utils.data import Dataset, DataLoader
# ... (other imports)

# ... (class and function definitions)

def main():
    # ... (main logic of your script, e.g., training and testing the model)

if __name__ == '__main__':
    mp.set_start_method('spawn')
    main()

By wrapping your main script logic inside a main() function and calling it inside the if __name__ == '__main__': block, you ensure that the multiprocessing start method is set correctly before running the main part of your script.

It appears that you are trying to use multiprocessing with DataLoader on Windows. The RuntimeError occurs because you need to wrap the main part of your script inside the if __name__ == '__main__': block to avoid issues with the multiprocessing module on Windows.

Here's an updated version of your script with the required changes:

pythonCopy code
import pandas as pd
import torch
from torch.utils.data import Dataset, DataLoader
from sklearn.metrics import f1_score
from sklearn.model_selection import StratifiedKFold
from transformers import RobertaTokenizer, RobertaForSequenceClassification, AdamW, get_linear_schedule_with_warmup
import torch.multiprocessing as mp
# ... (CustomDataset and create_news_dataset definitions)

def main():
    # ... (main logic of your script, e.g., training and testing the model)
    # Check if GPU is available and set device
    # Load the training data
    # Initialize the RoBERTa tokenizer
    # Calculate the class weights to handle class imbalance
    # Set up k-fold cross-validation
    # Start k-fold cross-validation
    # ... (the rest of your script)

if __name__ == '__main__':
    mp.set_start_method('spawn')
    main()

Make sure to put all the code that runs the main logic of your script inside the main() function, which will be called inside the if __name__ == '__main__': block. This should resolve the RuntimeError you are encountering.

Here's a detailed version of the main() function and the if __name__ == '__main__': block. I have moved the relevant parts of your script into the main() function:

pythonCopy code
def main():
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    train_df = pd.read_csv('train.csv')
    tokenizer = RobertaTokenizer.from_pretrained('roberta-base')

    class_weights = {}
    total = sum([14146, 10961, 9379, 8946, 2461, 1022, 278, 206])
    for i in range(8):
        class_weights[i] = total / (8 * [14146, 10961, 9379, 8946, 2461, 1022, 278, 206][i])
    class_weights = torch.tensor(list(class_weights.values())).to(device)

    epochs = 3
    accumulation_steps = 4

    k_folds = 5
    kf = StratifiedKFold(n_splits=k_folds, shuffle=True, random_state=42)

    # Start k-fold cross-validation
    for fold, (train_indices, val_indices) in enumerate(kf.split(train_df, train_df["label"])):
        # ... (The rest of your script)

if __name__ == '__main__':
    mp.set_start_method('spawn')
    main()

In this modified version of your script, I have defined a main() function that contains the main logic of your script, including setting the device, loading the training data, initializing the tokenizer, calculating the class weights, setting up k-fold cross-validation, and running the cross-validation loop.

Then, I have placed the main() function call inside the if __name__ == '__main__': block. This ensures that the main() function is only executed when your script is run as the main module. This is necessary when using multiprocessing on Windows to avoid issues with spawning new processes.

By structuring your script this way, you should be able to resolve the RuntimeError you were encountering earlier.