Count tokens in datasets and plot distribution
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Dataset Token Distribution is a tool designed to analyze and visualize the distribution of tokens within datasets. It helps users understand the composition of their data by counting tokens and plotting their frequency distribution. This tool is particularly useful for Natural Language Processing (NLP) tasks, where token distribution insights can guide model training, data preprocessing, and balancing strategies.
• Token Counting: Automatically counts the occurrences of each token in the dataset.
• Distribution Plotting: Generates visual representations of token frequencies for easier interpretation.
• Customizable Tokenization: Supports various tokenization methods to suit different datasets.
• Data Filtering: Allows users to filter tokens based on frequency thresholds.
• Export Options: Enables exporting of both token counts and distribution plots for further analysis.
• Multi-Format Support: Works with diverse data formats, including CSV, JSON, and text files.
• Bias Detection: Highlights imbalances in token distribution to identify potential dataset biases.
What file formats does Dataset Token Distribution support?
The tool supports CSV, JSON, and plain text files. Additional formats can be added through custom processing.
How do I handle extremely large datasets?
For large datasets, consider sampling a representative subset or using distributed processing frameworks to avoid memory issues.
Can I customize the visualization style?
Yes, the tool allows customization of colors, fonts, and plot types to suit your presentation needs.
How do I troubleshoot token counting issues?
Ensure your data is properly formatted and tokenized. Check for special characters or encoding problems that may affect token recognition.