AIDir.app
  • Hot AI Tools
  • New AI Tools
  • AI Tools Category
AIDir.app
AIDir.app

Save this website for future use! Free to use, no login required.

About

  • Blog

Β© 2025 β€’ AIDir.app All rights reserved.

  • Privacy Policy
  • Terms of Service
Home
Dataset Creation
Dataset Token Distribution

Dataset Token Distribution

Count tokens in datasets and plot distribution

You May Also Like

View All
πŸ“Š

Fast

Organize and invoke AI models with Flow visualization

0
πŸ”₯

Datasette Thebloke

Browse TheBloke models' history

8
🌐

πŸŒπŸ“„πŸ’ΎπŸ›οΈWebCopyData.Gov

Browse and search datasets

1
🐢

Convert to Safetensors

Convert a model to Safetensors and open a PR

0
πŸ‘€

Feedback App

Provide feedback on AI responses to prompts

0
🌍

Space to Dataset Saver

Save user inputs to datasets on Hugging Face

31
πŸ‘

Sarthaksavvy Flux Lora Train

Train a model using custom data

1
πŸ“ˆ

Trending Repos

Display trending datasets and spaces

2
πŸ’»

Function Calling Datasets Explorer

Browse and view Hugging Face datasets from a collection

7
πŸ’»

Domain Specific Seed

Create a domain-specific dataset seed

0
πŸ¦€

Viewer Embed

Display instructional dataset

0
πŸ“Š

FastGPT

Manage and orchestrate AI workflows and datasets

0

What is Dataset Token Distribution ?

Dataset Token Distribution is a tool designed to analyze and visualize the distribution of tokens within datasets. Tokens can be words, characters, or subwords, depending on the tokenization method used. This tool helps users understand the composition of their datasets by counting token occurrences and plotting their frequency distribution. It is particularly useful for natural language processing (NLP) tasks where token distribution insights can inform model training and data preprocessing.

Features

  • Token Counting: Automatically counts the occurrences of each token in the dataset.
  • Distribution Plotting: Generates visual representations (e.g., bar charts, histograms) to show token frequency.
  • Filtering Options: Allows users to filter tokens based on frequency thresholds or specific patterns.
  • Export Capabilities: Supports exporting token distributions as CSV files or images for further analysis.
  • Customizable Visualizations: Enables customization of plots (e.g., color schemes, axes labels) for better readability.
  • Performance Metrics: Provides metrics like top frequent tokens and long-tail distribution analysis.

How to use Dataset Token Distribution ?

  1. Install the Tool: Install the Dataset Token Distribution tool using the provided installation instructions.
  2. Load Your Dataset: Upload or load your dataset into the tool. Ensure the dataset is in a supported format (e.g., CSV, JSON).
  3. Process the Dataset: Run the tokenization process to count tokens and their frequencies.
  4. Visualize the Distribution: Use the tool to generate plots that show the token frequency distribution.
  5. Analyze the Results: Examine the plots to identify patterns, such as long-tail distributions or outlier tokens.
  6. Export Results: Save the token distribution data or visualizations for further analysis or reporting.

Frequently Asked Questions

What is a token in the context of Dataset Token Distribution?
A token is a basic unit of text, such as a word, character, or subword, depending on the tokenization method used. For example, in the sentence "Hello world," "Hello" and "world" are tokens.

How can I interpret the token distribution plot?
A token distribution plot shows the frequency of each token in your dataset. A long-tail distribution indicates that most tokens appear infrequently, while a few tokens appear very often. This can help identify common patterns or unusual outliers in your data.

Can I use Dataset Token Distribution for non-NLP tasks?
While Dataset Token Distribution is primarily designed for NLP tasks, it can be adapted for other datasets where tokenization is applicable, such as DNA sequences or code snippets. However, its effectiveness may vary depending on the use case.

Recommended Category

View All
β€‹πŸ—£οΈ

Speech Synthesis

🎡

Generate music for a video

πŸ–ΌοΈ

Image Captioning

πŸ“

Generate a 3D model from an image

❓

Visual QA

πŸ”

Detect objects in an image

πŸŽ™οΈ

Transcribe podcast audio to text

πŸ—‚οΈ

Dataset Creation

πŸ“

Model Benchmarking

πŸ–ΌοΈ

Image Generation

⬆️

Image Upscaling

🌐

Translate a language in real-time

πŸ’»

Generate an application

✨

Restore an old photo

πŸ“Ή

Track objects in video