Improve language tag

README.md

@@ -1,119 +1,132 @@
----
-language: en
-license: mit
-library_name: transformers
-pipeline_tag: text-generation
-tags:
-  - text-generation
-  - ai-detection
-  - paraphrasing
-  - originality
-  - privacy
-datasets:
-  - checkgpt
-base_model: Qwen/Qwen2.5-3B-Instruct
-model_type: causal-lm
----
-
-# AuthorMist Originality
-
-[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-AuthorMist-blue)](https://huggingface.co/authormist/originality)
-[![License](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
-
-## Overview
-
-AuthorMist Originality is a specialized language model designed to transform AI-generated text into more human-like writing while preserving the original meaning. This model was developed using reinforcement learning techniques to specifically evade AI text detection systems, with a focus on Originality.ai's detection algorithms.
-
-The model is based on Qwen2.5-3B Instruct and has been fine-tuned using Group Relative Policy Optimization (GRPO) with detector feedback as a reward signal. AuthorMist Originality demonstrates strong performance in reducing detectability across multiple AI text detection systems while maintaining high semantic similarity with the original text.
-
-## Key Features
-
-- **Detector Evasion**: Trained specifically to evade Originality.ai's detection algorithms, with strong cross-detector generalization
-- **Meaning Preservation**: Maintains high semantic similarity (>0.94) with the original text
-- **Natural Output**: Produces fluent, coherent text that reads naturally
-- **Broad Applicability**: Effective across various domains including academic, technical, and creative writing
-
-## Model Details
-
-- **Base Model**: Qwen2.5-3B Instruct
-- **Training Method**: Reinforcement Learning with Group Relative Policy Optimization (GRPO)
-- **Training Data**: 10,000 human-written abstracts from the CheckGPT dataset with corresponding AI-generated versions
-- **Domains Covered**: Computer Science, Humanities, Social Sciences, Physics, and more
-- **Text Length Support**: Optimized for texts ranging from 100 to 500 words
-
-## Performance
-
-AuthorMist Originality demonstrates exceptional performance in evading AI text detection:
-
-- **Mean AUROC**: 0.49 across six major detection systems
-- **Mean F1-score**: 0.09 across all tested detectors
-- **Semantic Similarity**: >0.94 with original text
-
-The model shows particularly strong performance against:
-- Hello SimpleAI (AUROC: 0.07)
-- Sapling (AUROC: 0.13)
-- Winston.ai (AUROC: 0.35)
-
-## Usage
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-# Load model and tokenizer
-model_name = "authormist/authormist-originality"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
-
-# Prepare input text
-ai_text = "Your AI-generated text here..."
-prompt = f"""Please paraphrase the following text to make it more human-like while preserving the original meaning:
-
-{ai_text}
-
-Paraphrased text:"""
-
-# Generate paraphrased text
-inputs = tokenizer(prompt, return_tensors="pt")
-outputs = model.generate(
-    inputs.input_ids,
-    max_new_tokens=512,
-    temperature=0.7,
-    top_p=0.9,
-    do_sample=True
-)
-paraphrased_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-print(paraphrased_text.split("Paraphrased text:")[1].strip())
-```
-
-## Ethical Considerations
-
-AuthorMist Originality is released for research purposes to advance understanding of AI text detection limitations and privacy-preserving technologies. We acknowledge the dual-use nature of this technology and emphasize the following ethical considerations:
-
-1. **Academic Integrity**: This model should not be used to misrepresent AI-generated content as human-written in academic settings where such distinctions are ethically relevant.
-
-2. **Transparency**: We encourage users to maintain transparency about the use of AI assistance in content creation, even when using privacy-enhancing tools like AuthorMist.
-
-3. **Privacy Protection**: The primary legitimate use case for this technology is protecting author privacy and preventing unfair discrimination against AI-assisted writing in contexts where such assistance is permissible.
-
-4. **Research Value**: This model provides valuable insights into the limitations of current AI detection systems and contributes to the ongoing research dialogue about AI text detection and privacy.
-
-## Citation
-
-If you use AuthorMist Originality in your research, please cite our paper:
-
-```bibtex
-@article{authormist2025,
-  title={AuthorMist: Evading AI Text Detectors with Reinforcement Learning},
-  author={David, Isaac and Gervais, Arthur},
-  journal={arXiv preprint},
-  year={2025}
-}
-```
-
-## License
-
-This model is released under the [MIT License](https://opensource.org/licenses/MIT).
-
-## Acknowledgments
-
+---
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+license: mit
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- text-generation
+- ai-detection
+- paraphrasing
+- originality
+- privacy
+datasets:
+- checkgpt
+base_model: Qwen/Qwen2.5-3B-Instruct
+model_type: causal-lm
+---
+
+# AuthorMist Originality
+
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-AuthorMist-blue)](https://huggingface.co/authormist/originality)
+[![License](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
+
+## Overview
+
+AuthorMist Originality is a specialized language model designed to transform AI-generated text into more human-like writing while preserving the original meaning. This model was developed using reinforcement learning techniques to specifically evade AI text detection systems, with a focus on Originality.ai's detection algorithms.
+
+The model is based on Qwen2.5-3B Instruct and has been fine-tuned using Group Relative Policy Optimization (GRPO) with detector feedback as a reward signal. AuthorMist Originality demonstrates strong performance in reducing detectability across multiple AI text detection systems while maintaining high semantic similarity with the original text.
+
+## Key Features
+
+- **Detector Evasion**: Trained specifically to evade Originality.ai's detection algorithms, with strong cross-detector generalization
+- **Meaning Preservation**: Maintains high semantic similarity (>0.94) with the original text
+- **Natural Output**: Produces fluent, coherent text that reads naturally
+- **Broad Applicability**: Effective across various domains including academic, technical, and creative writing
+
+## Model Details
+
+- **Base Model**: Qwen2.5-3B Instruct
+- **Training Method**: Reinforcement Learning with Group Relative Policy Optimization (GRPO)
+- **Training Data**: 10,000 human-written abstracts from the CheckGPT dataset with corresponding AI-generated versions
+- **Domains Covered**: Computer Science, Humanities, Social Sciences, Physics, and more
+- **Text Length Support**: Optimized for texts ranging from 100 to 500 words
+
+## Performance
+
+AuthorMist Originality demonstrates exceptional performance in evading AI text detection:
+
+- **Mean AUROC**: 0.49 across six major detection systems
+- **Mean F1-score**: 0.09 across all tested detectors
+- **Semantic Similarity**: >0.94 with original text
+
+The model shows particularly strong performance against:
+- Hello SimpleAI (AUROC: 0.07)
+- Sapling (AUROC: 0.13)
+- Winston.ai (AUROC: 0.35)
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model and tokenizer
+model_name = "authormist/authormist-originality"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Prepare input text
+ai_text = "Your AI-generated text here..."
+prompt = f"""Please paraphrase the following text to make it more human-like while preserving the original meaning:
+
+{ai_text}
+
+Paraphrased text:"""
+
+# Generate paraphrased text
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(
+    inputs.input_ids,
+    max_new_tokens=512,
+    temperature=0.7,
+    top_p=0.9,
+    do_sample=True
+)
+paraphrased_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(paraphrased_text.split("Paraphrased text:")[1].strip())
+```
+
+## Ethical Considerations
+
+AuthorMist Originality is released for research purposes to advance understanding of AI text detection limitations and privacy-preserving technologies. We acknowledge the dual-use nature of this technology and emphasize the following ethical considerations:
+
+1. **Academic Integrity**: This model should not be used to misrepresent AI-generated content as human-written in academic settings where such distinctions are ethically relevant.
+
+2. **Transparency**: We encourage users to maintain transparency about the use of AI assistance in content creation, even when using privacy-enhancing tools like AuthorMist.
+
+3. **Privacy Protection**: The primary legitimate use case for this technology is protecting author privacy and preventing unfair discrimination against AI-assisted writing in contexts where such assistance is permissible.
+
+4. **Research Value**: This model provides valuable insights into the limitations of current AI detection systems and contributes to the ongoing research dialogue about AI text detection and privacy.
+
+## Citation
+
+If you use AuthorMist Originality in your research, please cite our paper:
+
+```bibtex
+@article{authormist2025,
+  title={AuthorMist: Evading AI Text Detectors with Reinforcement Learning},
+  author={David, Isaac and Gervais, Arthur},
+  journal={arXiv preprint},
+  year={2025}
+}
+```
+
+## License
+
+This model is released under the [MIT License](https://opensource.org/licenses/MIT).
+
+## Acknowledgments
+
 We thank the developers of Qwen2.5 for the base model and the creators of the CheckGPT dataset for providing valuable training data.