meta data block is added

README.md

@@ -1,63 +1,76 @@
-# AbuseBERT
-
-## Model Description
-
-**AbuseBERT** is a **BERT-based classification model** fine-tuned for **abusive language detection**, optimized for **cross-dataset generalization**.
-
-> Abusive language detection models often suffer from poor generalization due to **sampling and lexical biases** in individual datasets. Our approach addresses this by integrating **ten publicly available abusive language datasets**, harmonizing labels and preprocessing textual samples to create a **broader and more representative training distribution**.
-
-**Key Findings:**
-- Individual dataset models: average F1 = **0.60**  
-- Integrated model: F1 = **0.84**  
-- Dataset contribution to performance improvements correlates with **lexical diversity (0.71 correlation)**  
-- Integration exposes models to diverse abuse patterns, enhancing **real-world generalization**
-
----
-
-## Conclusion / Takeaways
-
-- No single dataset captures the full spectrum of abusive language; each dataset reflects a **limited slice** of the problem space.  
-- Systematically integrating ten heterogeneous datasets significantly improves classification performance on a **held-out benchmark**.  
-- Lexically dissimilar datasets contribute more to **enhancing generalization**.  
-- The integrated model demonstrates superior **cross-dataset performance** compared to models trained on individual datasets.
-
----
-
-## Paper Reference
-
-Samaneh Hosseini Moghaddam, Kelly Lyons, Frank Rudzicz, Cheryl Regehr, Vivek Goel, Kaitlyn Regehr,  
-“**Enhancing machine learning in abusive language detection with dataset aggregation**,” in *Proc. 35th IEEE Int. Conf. Collaborative Advances in Software Computing (CASC)*, 2025.
-
----
-
-## Intended Use
-
-**Recommended:**
-- Detecting abusive language in text from social media or online platforms
-- Research on bias mitigation and cross-dataset generalization
-- Supporting safe and inclusive online environments
-
-**Not Recommended:**
-- Fully automated moderation without human oversight
-- High-stakes legal or policy decisions
-
----
-
-## Usage Example
-
-```python
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch
-
-# Load model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained("Samanehmoghaddam/AbuseBERT")
-model = AutoModelForSequenceClassification.from_pretrained("Samanehmoghaddam/AbuseBERT")
-
-# Sample input
-text = "Your example text here."
-inputs = tokenizer(text, return_tensors="pt")
-outputs = model(**inputs)
-
-# Predicted label
-predicted_label = torch.argmax(outputs.logits, dim=1).item()
-print(f"Predicted label: {predicted_label}")
+---
+language: en
+tags:
+- text-classification
+- abusive-language
+- hate-speech
+- toxicity
+- cyberviolence
+- abusive-language-detection
+- BERT
+license: mit
+---
+
+# AbuseBERT
+
+## Model Description
+
+**AbuseBERT** is a **BERT-based classification model** fine-tuned for **abusive language detection**, optimized for **cross-dataset generalization**.
+
+> Abusive language detection models often suffer from poor generalization due to **sampling and lexical biases** in individual datasets. Our approach addresses this by integrating **ten publicly available abusive language datasets**, harmonizing labels and preprocessing textual samples to create a **broader and more representative training distribution**.
+
+**Key Findings:**
+- Individual dataset models: average F1 = **0.60**  
+- Integrated model: F1 = **0.84**  
+- Dataset contribution to performance improvements correlates with **lexical diversity (0.71 correlation)**  
+- Integration exposes models to diverse abuse patterns, enhancing **real-world generalization**
+
+---
+
+## Conclusion / Takeaways
+
+- No single dataset captures the full spectrum of abusive language; each dataset reflects a **limited slice** of the problem space.  
+- Systematically integrating ten heterogeneous datasets significantly improves classification performance on a **held-out benchmark**.  
+- Lexically dissimilar datasets contribute more to **enhancing generalization**.  
+- The integrated model demonstrates superior **cross-dataset performance** compared to models trained on individual datasets.
+
+---
+
+## Paper Reference
+
+Samaneh Hosseini Moghaddam, Kelly Lyons, Frank Rudzicz, Cheryl Regehr, Vivek Goel, Kaitlyn Regehr,  
+“**Enhancing machine learning in abusive language detection with dataset aggregation**,” in *Proc. 35th IEEE Int. Conf. Collaborative Advances in Software Computing (CASC)*, 2025.
+
+---
+
+## Intended Use
+
+**Recommended:**
+- Detecting abusive language in text from social media or online platforms
+- Research on bias mitigation and cross-dataset generalization
+- Supporting safe and inclusive online environments
+
+**Not Recommended:**
+- Fully automated moderation without human oversight
+- High-stakes legal or policy decisions
+
+---
+
+## Usage Example
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("Samanehmoghaddam/AbuseBERT")
+model = AutoModelForSequenceClassification.from_pretrained("Samanehmoghaddam/AbuseBERT")
+
+# Sample input
+text = "Your example text here."
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+
+# Predicted label
+predicted_label = torch.argmax(outputs.logits, dim=1).item()
+print(f"Predicted label: {predicted_label}")