Update README.md

README.md

@@ -9,7 +9,7 @@
 ## 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 
-from [OpenWebText](https://huggingface.co/datasets/Skylion007/openwebtext)
+from [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.
@@ -35,16 +35,16 @@ Please refer the following google colab demo to see how to locally run the downl
 ## 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 distinguish the human-text and 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 2) Update the paraphraser** During training, the paraphraser will do paraphrasing on the AI-text generated in **Step 1**. And then colect 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 parphraser 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).
 
 ## 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 
+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 
 are necessary as RADAR cannot always make correct predictions.