Update README.md

README.md

@@ -8,13 +8,14 @@ pipeline_tag: text-classification
 
 ## Model Details
 
-RADAR-Vicuna-7B is an AI-text detector trained via adversrial learning between the detector and a paraphraser on human-text corpus([OpenWebText](https://huggingface.co/datasets/Skylion007/openwebtext)) and ai-text corpus generated 
+RADAR-Vicuna-7B is an AI-text detector trained via adversarial learning between the detector and a paraphraser on human-text corpus ([OpenWebText](https://huggingface.co/datasets/Skylion007/openwebtext)) and AI-text corpus generated 
 based on [OpenWebText](https://huggingface.co/datasets/Skylion007/openwebtext).
 
 - **Developed by:** [TrustSafeAI](https://huggingface.co/TrustSafeAI)
-- **Model type:** An encoder-only language model based on the transformer architecture.
-- **License:** Non-commercial license
-- **Trained from model:** [RoBERTa](https://arxiv.org/abs/1907.11692).
+- **Model type:** An encoder-only language model based on the transformer architecture (RoBERTa).
+- **License:** [Non-commercial license](https://huggingface.co/lmsys/vicuna-7b-v1.1#model-details) (inherited from Vicuna-7B-v1.1)
+- **Trained from model:** [RoBERTa](https://arxiv.org/abs/1907.11692)
+  
 
 ### Model Sources
 
@@ -24,7 +25,7 @@ based on [OpenWebText](https://huggingface.co/datasets/Skylion007/openwebtext).
 
 ## Uses
 Users could use this detector to assist them in detecting text generated by large language models.
-Please be noted that this detector is trained on ai-text generated by Vicuna-7B-v1.1. As the model only supports [non-commercial use](https://huggingface.co/lmsys/vicuna-7b-v1.1#model-details), the intended users are **not allowed to involve this detector into commercial activities**.
+Please note that this detector is trained on AI-text generated by Vicuna-7B-v1.1. As the model only supports [non-commercial use](https://huggingface.co/lmsys/vicuna-7b-v1.1#model-details), the intended users are **not allowed to involve this detector into commercial activities**.
 
 ## Get Started with the Model
 Please refer to the following guidelines to see how to locally run the downloaded model or use our API service hosted on Huggingface Space.
@@ -34,15 +35,15 @@ Please refer to the following guidelines to see how to locally run the downloade
 ## Training Pipeline
 
 We propose adversarial learning between a paraphraser and our detector. The paraphraser's goal is to make the AI-generated text more like human-writen and the detector's goal is to 
-promote it's ability to identify the ai-text.
+promote it's ability to identify the AI-text.
 
-- **(Step 1) Training Data preparation**: Before training, we use Vicuna-7B to generate AI-text by performing text-completion based on the prefix span of human-text in [OpenWebText](https://huggingface.co/datasets/Skylion007/openwebtext).
+- **(Step 1) Training Data preparation**: Before training, we use Vicuna-7B to generate AI-text by performing text completion based on the prefix span of human-text in [OpenWebText](https://huggingface.co/datasets/Skylion007/openwebtext).
 
-- **(Step 2) Update the paraphraser** During training, the paraphraser will do paraphrasing on the AI-text generated in **Step 1**. And then collect the reward returned by the detector to update the parphraser using Proxy Proximal Optimization loss.
+- **(Step 2) Update the paraphraser** During training, the paraphraser will do paraphrasing on the AI-text generated in **Step 1**. And then collect the reward returned by the detector to update the paraphraser using Proxy Proximal Optimization loss.
 
-- **(Step 3) Update the detector** The detector is optimized using the logistic loss on the human-text, ai-text and paraphrased ai-text.
+- **(Step 3) Update the detector** The detector is optimized using the logistic loss on the human-text, AI-text and paraphrased AI-text.
 
-See more details in the Section 3 and 4 of this [paper](https://arxiv.org/pdf/2307.03838.pdf).
+See more details in Sections 3 and 4 of this [paper](https://arxiv.org/pdf/2307.03838.pdf).
 
 ## Ethical Considerations
 We suggest users use our tool to assist with identifying AI-written content at scale and with discretion. If the detection result is to be used as evidence, further validation steps