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--- |
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license: apache-2.0 |
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task_categories: |
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- text-classification |
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- question-answering |
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language: |
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- en |
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size_categories: |
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- 100K<n<1M |
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--- |
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# CuratedTextCorpus |
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<!-- markdownlint-disable first-line-h1 --> |
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<!-- markdownlint-disable html --> |
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<!-- markdownlint-disable no-duplicate-header --> |
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<div align="center"> |
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<img src="figures/fig1.png" width="60%" alt="CuratedTextCorpus" /> |
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</div> |
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<hr> |
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<div align="center" style="line-height: 1;"> |
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<a href="LICENSE" style="margin: 2px;"> |
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<img alt="License" src="figures/fig2.png" style="display: inline-block; vertical-align: middle;"/> |
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</a> |
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</div> |
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## 1. Introduction |
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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. |
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<p align="center"> |
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<img width="80%" src="figures/fig3.png"> |
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</p> |
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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. |
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Beyond improved deduplication, this version also offers reduced noise levels, better annotation consistency, and enhanced domain coverage. |
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## 2. Quality Metrics |
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### Comprehensive Quality Assessment |
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<div align="center"> |
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| | Metric | Baseline | v1.0 | v2.0 | CuratedTextCorpus | |
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|---|---|---|---|---|---| |
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| **Data Completeness** | Completeness | 0.821 | 0.855 | 0.871 | 0.877 | |
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| | Consistency | 0.756 | 0.782 | 0.801 | 0.806 | |
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| | Accuracy | 0.689 | 0.721 | 0.745 | 0.751 | |
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| **Data Validity** | Validity | 0.812 | 0.834 | 0.856 | 0.861 | |
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| | Uniqueness | 0.901 | 0.925 | 0.941 | 0.945 | |
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| | Timeliness | 0.667 | 0.698 | 0.721 | 0.727 | |
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| **Data Integrity** | Integrity | 0.778 | 0.801 | 0.823 | 0.828 | |
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| | Relevance | 0.712 | 0.738 | 0.761 | 0.766 | |
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| | Coverage | 0.645 | 0.678 | 0.702 | 0.708 | |
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| **Additional Metrics** | Conformity | 0.834 | 0.856 | 0.878 | 0.883 | |
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| | Precision | 0.723 | 0.751 | 0.776 | 0.782 | |
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| | Reliability | 0.789 | 0.812 | 0.834 | 0.839 | |
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</div> |
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### Overall Quality Summary |
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The CuratedTextCorpus demonstrates exceptional quality across all evaluated metrics, with particularly strong results in completeness and integrity assessments. |
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## 3. Data Access & API |
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We provide direct access to the dataset through our data portal. Please check our official documentation for API access details. |
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## 4. How to Use |
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Please refer to our documentation for information on loading and using CuratedTextCorpus. |
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Usage recommendations for CuratedTextCorpus: |
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1. Preprocessing scripts are included for common NLP tasks. |
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2. Balanced sampling utilities are available for imbalanced labels. |
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The data format follows standard HuggingFace datasets conventions with train/validation/test splits. |
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### Loading the Dataset |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("username/CuratedTextCorpus") |
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``` |
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### Data Fields |
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The dataset includes the following fields: |
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- `text`: The main text content |
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- `label`: Classification label (if applicable) |
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- `metadata`: Additional context information |
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### Recommended Preprocessing |
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```python |
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from transformers import AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") |
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def preprocess(examples): |
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return tokenizer(examples["text"], truncation=True, padding=True) |
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tokenized_dataset = dataset.map(preprocess, batched=True) |
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``` |
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## 5. License |
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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. |
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## 6. Contact |
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If you have any questions, please raise an issue on our repository or contact us at data@curatedtextcorpus.ai. |
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