Koushim/processed-jigsaw-toxic-comments

Processed Jigsaw Toxic Comments Dataset

This is a preprocessed and tokenized version of the original Jigsaw Toxic Comment Classification Challenge dataset, prepared for multi-label toxicity classification using transformer-based models like BERT.

⚠️ Important Note: I am not the original creator of the dataset. This dataset is a cleaned and restructured version made for quick use in PyTorch deep learning models.

📦 Dataset Features

Each example contains:

• —text: The original user comment
• —labels: A list of 7 binary float values indicating presence of toxicity categories
• —input_ids, attention_mask: Tokenized fields using bert-base-uncased (max length 128)

Toxicity Categories:

1. 1.toxicity
2. 2.severe_toxicity
3. 3.obscene
4. 4.threat
5. 5.insult
6. 6.identity_attack
7. 7.sexual_explicit

🧪 Dataset Splits

🔧 Processing Details

1. 1.Original Source: Manually downloaded from Kaggle
2. 2.Preprocessing:
3. 3.Combined multiple toxicity columns into a single labels vector
4. 4.Converted label values to floats (0.0 or 1.0)
5. 5.Tokenization:
6. 6.Used Hugging Face bert-base-uncased tokenizer
7. 7.Applied padding and truncation to max length of 128
8. 8.Formatting:
9. 9.Final dataset set to return PyTorch input_ids, attention_mask, and labels

💡 Usage Example

from datasets import load_dataset

dataset = load_dataset("Koushim/processed-jigsaw-toxic-comments")

from torch.utils.data import DataLoader

train_loader = DataLoader(dataset["train"], batch_size=32, shuffle=True)

batch = next(iter(train_loader))
print(batch['input_ids'].shape)  # torch.Size([32, 128])
print(batch['labels'].shape)     # torch.Size([32, 7])

📚 Citation

If you use this dataset, please cite the original Jigsaw authors:

@misc{jigsawtoxic,
  title={Toxic Comment Classification Challenge},
  author={Jigsaw and Google},
  year={2018},
  url={https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge}
}

🙏 Acknowledgements

• —Original dataset by Jigsaw/Google
• —Processing, formatting, and tokenization by Koushik