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| license: apache-2.0 |
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| # Semantic Integrity Analysis Dataset |
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| ## Overview |
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| This dataset is designed for detecting semantic integrity violations between sentence pairs. |
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| Each data instance contains two sentences and a label indicating the semantic relationship between them. |
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| The dataset supports multi-class text pair classification. |
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| ## Task Description |
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| Given two sentences (sentence1 and sentence2), the model must classify the relationship as: |
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| - 0 → Contradiction |
| - 1 → Inconsistency |
| - 2 → Duplication |
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| This task is similar to Natural Language Inference (NLI), but focuses on semantic validation within structured documents. |
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| ## Dataset Structure |
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| Each row contains: |
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| - sentence1 (string) |
| - sentence2 (string) |
| - label (integer) |
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| Example: |
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| sentence1: "The report was submitted in 2022." |
| sentence2: "The report was submitted in 2023." |
| label: 1 |
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| ## Label Description |
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| | Label | Category | Meaning | |
| |-------|-----------------|---------| |
| | 0 | Contradiction | Opposite meaning between sentences | |
| | 1 | Inconsistency | Conflicting details or mismatched facts | |
| | 2 | Duplication | Same or nearly same meaning | |
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| ## Data Source |
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| The dataset was created from four structured documents (doc1, doc2, doc3, doc4). |
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| Sentence pairs were extracted and manually annotated. |
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| ## Annotation Process |
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| Annotation was performed manually based on semantic relationship guidelines. |
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| Each sentence pair was reviewed and labeled into one of three categories. |
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| ## Intended Use |
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| This dataset can be used for: |
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| - Fine-tuning transformer models |
| - Semantic validation systems |
| - Document integrity checking |
| - NLP research on sentence-pair classification |
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| ## Limitations |
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| - Limited dataset size |
| - Domain-specific content may reduce generalization |
| - Manual annotation may introduce bias |
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| ## Ethical Considerations |
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| The dataset does not contain sensitive personal information. |
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| It is intended for research and educational use only. |
