Update README.md

README.md

@@ -1,3 +1,117 @@
----
-license: mit
----
+---
+language: en
+datasets:
+  - jigsaw-toxic-comment-classification-challenge
+tags:
+  - multi-label-classification
+  - toxicity-detection
+  - bert
+  - transformers
+  - pytorch
+license: apache-2.0
+model-index:
+  - name: BERT Multi-label Toxic Comment Classifier
+    results:
+      - task:
+          name: Multi-label Text Classification
+          type: multi-label-classification
+        dataset:
+          name: Jigsaw Toxic Comment Classification Challenge
+          type: jigsaw-toxic-comment-classification-challenge
+        metrics:
+          - name: F1 Score (Macro)
+            type: f1
+            value: 0.XX  # Replace with your actual score
+          - name: Accuracy
+            type: accuracy
+            value: 0.XX  # Replace with your actual score
+---
+
+# BERT Multi-label Toxic Comment Classifier
+
+This model is a fine-tuned [`bert-base-uncased`](https://huggingface.co/bert-base-uncased) transformer for **multi-label classification** on the [Jigsaw Toxic Comment Classification Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge) dataset.
+
+It predicts multiple toxicity-related labels per comment, including:
+- toxicity
+- severe toxicity
+- obscene
+- threat
+- insult
+- identity attack
+- sexual explicit
+
+## Model Details
+
+- **Base Model**: `bert-base-uncased`
+- **Task**: Multi-label text classification
+- **Dataset**: Jigsaw Toxic Comment Classification Challenge (processed version)
+- **Labels**: 7 toxicity-related categories
+- **Training Epochs**: 2
+- **Batch Size**: 16 (train), 64 (eval)
+- **Metrics**: Accuracy, Macro F1, Precision, Recall
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("Koushim/bert-multilabel-jigsaw-toxic-classifier")
+model = AutoModelForSequenceClassification.from_pretrained("Koushim/bert-multilabel-jigsaw-toxic-classifier")
+
+text = "You are a wonderful person!"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
+outputs = model(**inputs)
+
+# Sigmoid to get probabilities for each label
+import torch
+probs = torch.sigmoid(outputs.logits)
+print(probs)
+````
+
+## Labels
+
+| Index | Label            |
+| ----- | ---------------- |
+| 0     | toxicity         |
+| 1     | severe_toxicity |
+| 2     | obscene          |
+| 3     | threat           |
+| 4     | insult           |
+| 5     | identity_attack |
+| 6     | sexual_explicit |
+
+## Training Details
+
+* Loss Function: Binary Cross Entropy (via `BertForSequenceClassification` with `problem_type="multi_label_classification"`)
+* Optimizer: AdamW
+* Learning Rate: 2e-5
+* Evaluation Strategy: Epoch-based evaluation with early stopping on F1 score
+* Model Framework: PyTorch with Hugging Face Transformers
+
+## Repository Contents
+
+* `pytorch_model.bin` - trained model weights
+* `config.json` - model configuration
+* `tokenizer.json`, `vocab.txt` - tokenizer files
+* `README.md` - this file
+
+## How to Fine-tune or Train
+
+You can fine-tune this model using the Hugging Face `Trainer` API with your own dataset or the original Jigsaw dataset.
+
+## Citation
+
+If you use this model in your research or project, please cite:
+
+```
+@article{devlin2019bert,
+  title={BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding},
+  author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
+  journal={arXiv preprint arXiv:1810.04805},
+  year={2019}
+}
+```
+
+## License
+
+Apache 2.0 License