Update README.md

README.md

@@ -22,7 +22,7 @@ size_categories:
 ImageRewardDB is a comprehensive text-to-image comparison dataset, focusing on text-to-image human preference. 
 It consists of 137k pairs of expert comparisons, based on text prompts and corresponding model outputs from DiffusionDB. 
 To build the ImageRewadDB, we design a pipeline tailored for it, establishing criteria for quantitative assessment and 
-annotator training, optimizing labeling experience, and ensuring quality validation. And ImageRewardDB is now public available at 
+annotator training, optimizing labeling experience, and ensuring quality validation. And ImageRewardDB is now publicly available at 
 [🤗 Hugging Face Dataset](https://huggingface.co/datasets/wuyuchen/ImageRewardDB).
 
 ### Languages
@@ -33,7 +33,7 @@ The text in the dataset is all in English.
 
 Considering that the ImageRewardDB contains a large number of images, we provide four subsets in different scales to support different needs.
 For all subsets, the validation and test splits remain the same. The validation split(1.08GB) contains 412 prompts and 3.2K images and 
-the test(1.14GB) split cotains 466 prompts and 3.4K images. The information of the train split in different scales is as following:
+the test(1.14GB) split contains 466 prompts and 3.4K images. The information on the train split in different scales is as follows:
 
 |Subset|Num of Images|Num of Prompts|Size|
 |:--|--:|--:|--:|
@@ -44,10 +44,10 @@ the test(1.14GB) split cotains 466 prompts and 3.4K images. The information of t
 
 ## Dataset Structure
 
-All the data in this repository is stored in a well organized way. The 62.6K images in ImageRewardDB are split into several folders, 
-stored in corresponding directories under "./images" according to its split. Each folder contains around 500 prompts, its corresponding 
+All the data in this repository is stored in a well-organized way. The 62.6K images in ImageRewardDB are split into several folders, 
+stored in corresponding directories under "./images" according to its split. Each folder contains around 500 prompts, their corresponding 
 images, and a JSON file. The JSON file links the image with its corresponding prompt and annotation.
-The file structure is as following:
+The file structure is as follows:
 ```
 # ImageRewardDB
 ./
@@ -70,8 +70,8 @@ The file structure is as following:
 ├── metadata-validation.parquet
 └── metadata-test.parquet
 ```
-The sub-folders have the name of {split_name}_{part_id}, and the JSON file have the same name as the sub-folder.
-Each image is a lossless WebP file, and has a unique name generated by [UUID](https://en.wikipedia.org/wiki/Universally_unique_identifier).
+The sub-folders have the name of {split_name}_{part_id}, and the JSON file has the same name as the sub-folder.
+Each image is a lossless WebP file and has a unique name generated by [UUID](https://en.wikipedia.org/wiki/Universally_unique_identifier).
 
 ### Data Instances
 
@@ -100,12 +100,12 @@ For instance, below is the image of `1b4b2d61-89c2-4091-a1c0-f547ad5065cb.webp`
 * image_amount_in_total: Total amount of images related to the prompt
 * rank: The relative rank of the image in all related images
 * overall_rating: The overall score of this image
-* image_text_alignment_rating: The score of how well the generated image matchs the given text
+* image_text_alignment_rating: The score of how well the generated image matches the given text
 * fidelity_rating: The score of whether the output image is true to the shape and characteristics that the object should have
 
 ### Data Splits
 
-As we mentioned above, all scales of the subsets we provided have three spilts of "train", "validtion", and "test". 
+As we mentioned above, all scales of the subsets we provided have three splits of "train", "validation", and "test". 
 And all the subsets share the same validation and test splits.
 
 ### Dataset Metadata
@@ -114,7 +114,7 @@ We also include three metadata tables `metadata-train.parquet`, `metadata-valida
 help you access and comprehend ImageRewardDB without downloading the Zip files.
 
 All the tables share the same schema, and each row refers to an image. The schema is shown below, 
-and actually the JSON files we mentioned above share the same schema:
+and actually, the JSON files we mentioned above share the same schema:
 
 |Column|Type|Description|
 |:---|:---|:---|
@@ -125,10 +125,10 @@ and actually the JSON files we mentioned above share the same schema:
 |`image_amount_in_total`|`int`| Total amount of images related to the prompt.|
 |`rank`|`int`| The relative rank of the image in all related images.|
 |`overall_rating`|`int`| The overall score of this image.
-|`image_text_alignment_rating`|`int`|The score of how well the generated image matchs the given text.|
+|`image_text_alignment_rating`|`int`|The score of how well the generated image matches the given text.|
 |`fidelity_rating`|`int`|The score of whether the output image is true to the shape and characteristics that the object should have.|
 
-Below are an example row from metadata-train.parquet.
+Below is an example row from metadata-train.parquet.
 
 |image_path|prompt_id|prompt|classification|image_amount_in_total|rank|overall_rating|image_text_alignment_rating|fidelity_rating|
 |:---|:---|:---|:---|:---|:---|:---|:---|:---|