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README.md

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+# SpanCNN Model for Toxic Text Classification
+
+## Overview
+
+The SpanCNN model is designed for toxic text classification, distinguishing between safe and toxic content. This model is part of the research presented in the paper titled [CMD: A Framework for Context-aware Model Self-Detoxification](https://arxiv.org/abs/2308.08295).
+
+## Model Details
+
+- **Input**: Text data
+- **Output**: Integer
+  - `0` represents **safe** content
+  - `1` represents **toxic** content
+
+## Usage
+
+To use the SpanCNN model for toxic text classification, follow the example below:
+
+```python
+from transformers import pipeline
+
+# Load the SpanCNN model
+classifier = pipeline("spancnn-classification", model="ZetangForward/SpanCNN", trust_remote_code=True)
+
+# Example 1: Safe text
+pos_text = "You look good today~!"
+result = classifier(pos_text)
+print(result)  # Output: 0 (safe)
+
+# Example 2: Toxic text
+neg_text = "You're too stupid, you're just like a fool"
+result = classifier(neg_text)
+print(result)  # Output: 1 (toxic)
+```
+
+## Citation
+
+If you find this model useful, please consider citing the original paper:
+
+```bibtex
+@article{tang2023detoxify,
+  title={Detoxify language model step-by-step},
+  author={Tang, Zecheng and Zhou, Keyan and Wang, Pinzheng and Ding, Yuyang and Li, Juntao and others},
+  journal={arXiv preprint arXiv:2308.08295},
+  year={2023}
+}
+```
+
+## Disclaimer
+
+While the SpanCNN model is effective in detecting toxic segments within text, we strongly recommend that users carefully review the results and exercise caution when applying this method in real-world scenarios. The model is not infallible, and its outputs should be validated in context-sensitive applications.