Update README.md

README.md

@@ -34,6 +34,56 @@ dataset_info:
     num_examples: 2666
   download_size: 6391314
   dataset_size: 17418436
+license: apache-2.0
+task_categories:
+- image-classification
+- image-to-text
+language:
+- en
+size_categories:
+- 10K<n<100K
 ---
+## Dataset Creation and Processing Overview
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+This dataset underwent a comprehensive process of loading, cleaning, processing, and preparing, incorporating a range of data manipulation and NLP techniques to optimize its utility for machine learning models, particularly in natural language processing.
+
+### Data Loading and Initial Cleaning
+- **Source**: Loaded from the Hugging Face dataset repository (`bprateek/amazon_product_description`).
+- **Conversion to Pandas DataFrame**: For ease of data manipulation.
+- **Null Value Removal**: Rows with null values in the 'About Product' column were discarded.
+
+### Data Cleaning and NLP Processing
+- **Sentence Extraction**: 'About Product' descriptions were split into sentences, identifying common phrases.
+- **Emoji and Special Character Removal**: A regex function removed these elements from the product descriptions.
+- **Common Phrase Elimination**: A function was used to strip common phrases from each product description.
+- **Improving Writing Standards**: Adjusted capitalization, punctuation, and replaced '&' with 'and' for better readability and formalization.
+
+### Sentence Similarity Analysis
+- **Model Application**: The pre-trained Sentence Transformer model 'all-MiniLM-L6-v2' was used.
+- **Sentence Comparison**: Identified the most similar sentence to each product name within the cleaned product descriptions.
+- **Integration of Results**: Added the most similar sentences as a new column 'Most_Similar_Sentence'.
+
+### Dataset Refinement
+- **Column Selection**: Retained relevant columns for final dataset.
+- **Image URL Processing**: Split multiple image URLs into individual URLs, removing specific unwanted URLs.
+- **Column Renaming**: Renamed 'Most_Similar_Sentence' to 'Description'.
+
+### Image Validation
+- **Image URL Validation**: Implemented a function to verify the validity of each image URL.
+- **Filtering Valid Images**: Retained only rows with valid image URLs.
+
+### Dataset Splitting for Machine Learning
+- **Creation of Train, Test, and Eval Sets**: Used scikit-learn's `train_test_split` for dataset division.
+
+### Hugging Face Dataset Preparation and Publishing
+- **Conversion to Dataset Objects**: Converted each Pandas DataFrame (train, test, eval) into Hugging Face `Dataset` objects.
+- **Dataset Dictionary Assembly**: Aggregated all splits into a `DatasetDict`.
+- **Publishing to Hugging Face Hub**: The dataset was named "amazon-products" and pushed to the Hub for community access.
+
+## Dataset Card Information
+- **Configs**: The dataset is split into train, test, and eval configurations, with paths specified for each.
+- **Features**: Includes fields for Product Name, Category, Description, Selling Price, Product Specification, and Image.
+- **Splits**: Detailed information on the number of bytes and examples for each dataset split.
+- **Sizes**: Download and total dataset size specifications are provided.
+
+For further details or to contribute to enhancing the dataset card, please refer to the [Hugging Face Dataset Card Contribution Guide](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards).