Update README.md

README.md

@@ -324,9 +324,10 @@ license: cc-by-4.0
 ---
 # High Quality Multilingual Sentences
 
-This dataset contains multilingual sentences derived from the [agentlans/LinguaNova](https://huggingface.co/datasets/agentlans/LinguaNova) dataset. It includes 1.58 million rows across 51 different languages, each in its own configuration.
+- This dataset contains multilingual sentences derived from the [agentlans/LinguaNova](https://huggingface.co/datasets/agentlans/LinguaNova) dataset.
+- It includes 1.58 million rows across 51 different languages, each in its own configuration.
 
-Example Row (from the `all` config):
+Example row (from the `all` config):
 ```json
 {
     "text": "امام جمعه اصفهان گفت: میزان نیاز آب شرب اصفهان ۱۱.۵ متر مکعب است که تمام استان اصفهان را پوشش میدهد و نسبت به قبل از انقلاب یکی از پیشرفتها در حوزه آب بوده است.",
@@ -341,9 +342,9 @@ Fields:
 
 ## Configurations
 
-Each individual language is available as a separate configuration, such as `ar`, `en`, etc. These configurations contain only sentences identified to be of that specific language by both the fastText and gcld3 models.
+Each individual language is available as a separate configuration, such as `ar`, `en`. These configurations contain only sentences identified to be of that specific language by both the fastText and gcld3 models.
 
-Example Row (from a language-specific config):
+Example row (from a language-specific config):
 ```json
 {
     "text": "Ne vienas asmuo yra apsaugotas nuo parazitų atsiradimo organizme."
@@ -356,7 +357,7 @@ Example Row (from a language-specific config):
 
 The `all` split was downloaded from the [agentlans/LinguaNova](https://huggingface.co/datasets/agentlans/LinguaNova) dataset.
 1. **Text Cleaning**: Raw text was cleaned by removing HTML tags, emails, emojis, hashtags, user handles, and URLs. Unicode characters and whitespace were normalized, and hyphenated words were handled to ensure consistency.
-2. **Sentence Segmentation**: Text was segmented into individual sentences using ICU’s `BreakIterator` class, which efficiently processed different languages and punctuation.
+2. **Sentence Segmentation**: Text was segmented into individual sentences using ICU's `BreakIterator` class, which efficiently processed different languages and punctuation.
 3. **Deduplication**: Duplicate entries were removed to maintain uniqueness and prevent redundancy in the dataset.
 
 ### Language Detection