toolevalxm/CuratedTextCorpus-TestRepo

CuratedTextCorpus

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1. Introduction

The CuratedTextCorpus dataset represents a major advancement in high-quality text data for NLP tasks. Through rigorous curation and validation processes, we have assembled a collection that meets the highest standards for machine learning applications. The dataset excels in text classification, question answering, and general language understanding tasks.

Compared to previous versions, this curated dataset shows significant improvements in data quality metrics. For instance, in duplicate detection tests, the deduplication rate has improved from 85% to 99.2%. This advancement stems from our enhanced preprocessing pipeline that now includes semantic similarity checks in addition to exact matching.

Beyond improved deduplication, this version also offers reduced noise levels, better annotation consistency, and enhanced domain coverage.

2. Quality Metrics

Comprehensive Quality Assessment

	Metric	Baseline	v1.0	v2.0	CuratedTextCorpus
Data Completeness	Completeness	0.821	0.855	0.871	0.877
	Consistency	0.756	0.782	0.801	0.806
	Accuracy	0.689	0.721	0.745	0.751
Data Validity	Validity	0.812	0.834	0.856	0.861
	Uniqueness	0.901	0.925	0.941	0.945
	Timeliness	0.667	0.698	0.721	0.727
Data Integrity	Integrity	0.778	0.801	0.823	0.828
	Relevance	0.712	0.738	0.761	0.766
	Coverage	0.645	0.678	0.702	0.708
Additional Metrics	Conformity	0.834	0.856	0.878	0.883
	Precision	0.723	0.751	0.776	0.782
	Reliability	0.789	0.812	0.834	0.839

Overall Quality Summary

The CuratedTextCorpus demonstrates exceptional quality across all evaluated metrics, with particularly strong results in completeness and integrity assessments.

3. Data Access & API

We provide direct access to the dataset through our data portal. Please check our official documentation for API access details.

4. How to Use

Please refer to our documentation for information on loading and using CuratedTextCorpus.

Usage recommendations for CuratedTextCorpus:

1. Preprocessing scripts are included for common NLP tasks.
2. Balanced sampling utilities are available for imbalanced labels.

The data format follows standard HuggingFace datasets conventions with train/validation/test splits.

Loading the Dataset

from datasets import load_dataset

dataset = load_dataset("username/CuratedTextCorpus")

Data Fields

The dataset includes the following fields:

• text: The main text content
• label: Classification label (if applicable)
• metadata: Additional context information

Recommended Preprocessing

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

def preprocess(examples):
    return tokenizer(examples["text"], truncation=True, padding=True)

tokenized_dataset = dataset.map(preprocess, batched=True)

5. License

This dataset is licensed under the Apache 2.0 License. The use of CuratedTextCorpus is subject to the license terms. Commercial use is permitted with attribution.

6. Contact

If you have any questions, please raise an issue on our repository or contact us at data@curatedtextcorpus.ai.