Update README.md

README.md

@@ -17,9 +17,9 @@ license: mit
 
 **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**.
+> Abusive language detection models often suffer from poor generalization due to **sampling and lexical biases** in individual datasets. Our approach addresses this by integrating **publicly available abusive language datasets**, harmonizing labels and preprocessing textual samples to create a **broader and more representative training distribution**.
 
-**Key Findings:**
+**Key Findings using 10 datasets:**
 - Individual dataset models: average F1 = **0.60**  
 - Integrated model: F1 = **0.84**  
 - Dataset contribution to performance improvements correlates with **lexical diversity (0.71 correlation)**  
@@ -46,9 +46,13 @@ Samaneh Hosseini Moghaddam, Kelly Lyons, Frank Rudzicz, Cheryl Regehr, Vivek Goe
 ## 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
+- Detecting abusive, offensive, or toxic language in text from social media, online forums, or messaging platforms.
+
+- Supporting research on online harassment, cyber violence, and hate speech analysis.
+
+- Assisting human moderators in content review or flagging potentially harmful content.
+
+- Evaluating trends, prevalence, or patterns of abusive language in large-scale textual datasets.
 
 **Not Recommended:**
 - Fully automated moderation without human oversight
@@ -59,18 +63,27 @@ Samaneh Hosseini Moghaddam, Kelly Lyons, Frank Rudzicz, Cheryl Regehr, Vivek Goe
 ## 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}")
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+
+# Load the model
+model_name = "Samanehmoghaddam/AbuseBERT"  
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Create a pipeline for text classification
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
+
+# Example texts to classify
+texts = [
+    "@user You are amazing!",
+    "@user You are stupid!",
+]
+
+# Run the classifier
+results = classifier(texts)
+
+# Print results
+for text, result in zip(texts, results):
+    print(f"Text: {text}")
+    print(f"Prediction: {result}")
+    print("-" * 40)