LuminaAI/CoLA_2_Class-GLUE-Benchmark

CoLA Text Classification RCL Dataset

Overview

This dataset contains textual data structured explicitly for standard text classification tasks using Lumina AI's Random Contrast Learning (RCL) algorithm via the PrismRCL application. Unlike LLM datasets, standard text classification datasets contain individual .txt files organized by class.

Dataset Structure

The dataset structure for text classification training:

CoLA.Classification/
    train/
        [class_1]/
            sample_001.txt
            sample_002.txt
            ...
        [class_2]/
            sample_001.txt
            sample_002.txt
            ...
    test/
        [class_1]/
            sample_001.txt
            sample_002.txt
            ...
        [class_2]/
            sample_001.txt
            sample_002.txt
            ...

• Classes: Folder names represent distinct text classes.
• Text Samples: Each .txt file represents a single textual data sample.

Text Data Preparation

For standard text classification datasets, PrismRCL has specific preparation requirements:

• Text samples must be in .txt format.
• Each file should contain a single data sample.
• File names must be unique across all class folders.

Usage (Text Classification-specific)

Use PrismRCL for training with text data:

C:\PrismRCL\PrismRCL.exe naivebayes rclticks=10 directional ^
  data=C:\path\to\CoLA.Classification\train testdata=C:\path\to\CoLA.Classification\test ^
  savemodel=C:\path\to\models\cola_text_model.classify ^
  log=C:\path\to\log_files stopwhendone

Explanation of Command

• naivebayes: Specifies Naive Bayes as the evaluation method for text classification.
• rclticks: Number of RCL iterations during training.
• directional: Maintains the order of words, essential for text classification.
• data & testdata: Paths to training and testing text datasets.
• savemodel: Output path for the trained classification model.
• log: Directory for storing log files.
• stopwhendone: Automatically terminates the session after training completion.

Auto Optimize

PrismRCL includes an Auto Optimize feature designed to automatically identify optimal training parameters for your specific dataset, significantly streamlining the model training process. This feature removes the need for manual parameter tuning by systematically evaluating your data to determine the most effective settings for evaluation method, rclticks, boxdown, and other relevant parameters.

How to Use Auto Optimize:

Run the following command with your dataset:

C:\PrismRCL\PrismRCL.exe auto-optimize data=C:\path\to\your_dataset\train log=C:\path\to\log_files

Explanation:

• auto-optimize: Initiates PrismRCL’s parameter optimization process.
• data: Path to your training dataset.
• log: Specifies the directory where PrismRCL will save a detailed summary file with optimal parameters determined by the optimization process.

After execution, PrismRCL generates an optimization summary file in your specified log directory (_optimize_summary_mm_dd_yy_hh_mm_ss.txt). This file will list the optimal parameters, which you should then apply in your training commands to achieve optimal model performance.

License

This dataset is licensed under the MIT License.

Original Source

Prepared explicitly by Lumina AI for RCL-based text classification training. Please credit Lumina AI when using this dataset in research or applications.

Additional Information

Refer to the PrismRCL Technical Documentation v2.6.2 for more detailed guidance on text classification data preparation and parameter specifications.