Update README.md

README.md

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----
-license: apache-2.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
-dataset_info:
-  features:
-  - name: text
-    dtype: string
-  - name: toxicity
-    dtype: float32
-  - name: severe_toxicity
-    dtype: float32
-  - name: obscene
-    dtype: float32
-  - name: threat
-    dtype: float32
-  - name: insult
-    dtype: float32
-  - name: identity_attack
-    dtype: float32
-  - name: sexual_explicit
-    dtype: float32
-  - name: labels
-    sequence: float64
-  - name: input_ids
-    sequence: int32
-  - name: token_type_ids
-    sequence: int8
-  - name: attention_mask
-    sequence: int8
-  splits:
-  - name: train
-    num_bytes: 2110899324
-    num_examples: 1804874
-  - name: validation
-    num_bytes: 113965680
-    num_examples: 97320
-  - name: test
-    num_bytes: 113712324
-    num_examples: 97320
-  download_size: 693905946
-  dataset_size: 2338577328
----
+---
+license: apache-2.0
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
+  - split: test
+    path: data/test-*
+dataset_info:
+  source_dataset: jigsaw-toxic-comment-classification-challenge
+  processed_by: Koushik (https://huggingface.co/datasets/Koushim)
+  tokenizer: bert-base-uncased
+  label_format: float multi-label binary vector
+  label_columns:
+    - toxicity
+    - severe_toxicity
+    - obscene
+    - threat
+    - insult
+    - identity_attack
+    - sexual_explicit
+  features:
+  - name: text
+    dtype: string
+  - name: toxicity
+    dtype: float32
+  - name: severe_toxicity
+    dtype: float32
+  - name: obscene
+    dtype: float32
+  - name: threat
+    dtype: float32
+  - name: insult
+    dtype: float32
+  - name: identity_attack
+    dtype: float32
+  - name: sexual_explicit
+    dtype: float32
+  - name: labels
+    sequence: float64
+  - name: input_ids
+    sequence: int32
+  - name: token_type_ids
+    sequence: int8
+  - name: attention_mask
+    sequence: int8
+  splits:
+  - name: train
+    num_bytes: 2110899324
+    num_examples: 1804874
+  - name: validation
+    num_bytes: 113965680
+    num_examples: 97320
+  - name: test
+    num_bytes: 113712324
+    num_examples: 97320
+  download_size: 693905946
+  dataset_size: 2338577328
+annotations_creators:
+  - crowdsourced
+language_creators:
+  - found
+language:
+  - en
+multilinguality:
+  - monolingual
+pretty_name: Processed Jigsaw Toxic Comment Classification
+tags:
+  - toxicity
+  - multi-label classification
+  - text classification
+  - NLP
+  - BERT
+  - hate speech
+size_categories:
+  - 1M<n<10M
+task_categories:
+  - text-classification
+task_ids:
+  - multi-label-classification
+---
+
+# Processed Jigsaw Toxic Comments Dataset
+
+This is a **preprocessed and tokenized** version of the original [Jigsaw Toxic Comment Classification Challenge](https://www.kaggle.com/competitions/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. `toxicity`
+2. `severe_toxicity`
+3. `obscene`
+4. `threat`
+5. `insult`
+6. `identity_attack`
+7. `sexual_explicit`
+
+---
+
+## 🧪 Dataset Splits
+
+| Split       | # Examples  |
+|-------------|-------------|
+| Train       | ~1.8M       |
+| Validation  | ~97K        |
+| Test        | ~97K        |
+
+---
+
+## 🔧 Processing Details
+
+1. **Original Source**: Manually downloaded from [Kaggle](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge)
+2. **Preprocessing**:
+   - Combined multiple toxicity columns into a single `labels` vector
+   - Converted label values to floats (0.0 or 1.0)
+3. **Tokenization**:
+   - Used Hugging Face `bert-base-uncased` tokenizer
+   - Applied padding and truncation to max length of 128
+4. **Formatting**:
+   - Final dataset set to return PyTorch `input_ids`, `attention_mask`, and `labels`
+
+---
+
+## 💡 Usage Example
+
+```python
+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:
+
+```bibtex
+@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](https://huggingface.co/koushik)