README.md

---
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
  - split: eval
    path: data/eval-*
datasets:
  - bprateek/amazon_product_description
dataset_info:
  features:
  - name: Product Name
    dtype: string
  - name: Category
    dtype: string
  - name: Description
    dtype: string
  - name: Selling Price
    dtype: string
  - name: Product Specification
    dtype: string
  - name: Image
    dtype: string
  splits:
  - name: train
    num_bytes: 12542887
    num_examples: 23993
  - name: test
    num_bytes: 3499375
    num_examples: 6665
  - name: eval
    num_bytes: 1376174
    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

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](https://huggingface.co/datasets/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).