Create README.md

README.md

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+---
+tags:
+- text-classification
+- toxicity-detection
+model-index:
+- name: toxicity-classifier
+  results: []
+---
+
+# Toxicity Classifier
+
+## Overview
+
+This model is a fine-tuned BERT model designed for detecting toxicity in text. It classifies input text as either "toxic" or "non-toxic" based on learned patterns from a diverse dataset of online comments and discussions. The model achieves high accuracy in identifying harmful language, making it suitable for content moderation tasks.
+
+## Model Architecture
+
+The model is based on the BERT (Bidirectional Encoder Representations from Transformers) architecture, specifically `bert-base-uncased`. It consists of 12 transformer layers, each with 12 attention heads and a hidden size of 768. The final layer is a classification head that outputs probabilities for the two classes: non-toxic (0) and toxic (1).
+
+## Intended Use
+
+This model is intended for use in applications requiring automated toxicity detection, such as:
+- Social media platforms for moderating user comments.
+- Online forums to flag potentially harmful content.
+- Customer support systems to identify abusive language in queries.
+
+It can be integrated into pipelines using the Hugging Face Transformers library. Example usage:
+
+```python
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model="your-username/toxicity-classifier")
+result = classifier("This is a harmful comment.")
+print(result)