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README.md

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+---
+license: apache-2.0
+task_categories:
+  - text-classification
+  - question-answering
+language:
+  - en
+size_categories:
+  - 100K<n<1M
+---
+# CuratedTextCorpus
+<!-- markdownlint-disable first-line-h1 -->
+<!-- markdownlint-disable html -->
+<!-- markdownlint-disable no-duplicate-header -->
+
+<div align="center">
+  <img src="figures/fig1.png" width="60%" alt="CuratedTextCorpus" />
+</div>
+<hr>
+
+<div align="center" style="line-height: 1;">
+  <a href="LICENSE" style="margin: 2px;">
+    <img alt="License" src="figures/fig2.png" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+
+## 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.
+
+<p align="center">
+  <img width="80%" src="figures/fig3.png">
+</p>
+
+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
+
+<div align="center">
+
+| | 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 |
+
+</div>
+
+### 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
+```python
+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
+```python
+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](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.

data.parquet

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metadata.json

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+{
+    "dataset_type": "text-corpus",
+    "version": "v8",
+    "num_samples": 562500,
+    "format": "parquet"
+}