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