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--- |
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paper: arxiv:2109.03794 |
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size_categories: |
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- n<1K |
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task_categories: |
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- token-classification |
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language: |
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- en |
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tags: |
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- pipeline-numbers |
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- ner |
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- p-and-id |
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--- |
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# Digitize-PID: Pipeline numbers (NER) |
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**Note**: *I am not the author of this dataset* |
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Named Entity Recognition dataset for extracting pipeline numbers from full text of P&ID |
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(Piping and Instrumentation Diagram) documents. |
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## Dataset Details |
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### Dataset Description |
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Pipeline numbers are structured identifiers in engineering documents: |
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- Example Format: `A-123-BC` (3-5 segments with a separator such as `-`, ` `, or `_`) |
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- Use case: Automated extraction from P&ID document text |
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- Domain: Process and piping industry |
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### Data Fields |
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- `id`: Unique example identifier |
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- `tokens`: List of tokenized words/punctuation |
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- `labels`: BIO tags for each token |
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- `pipeline_numbers`: Ground truth pipeline numbers |
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- `full_text`: Original text |
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### Label Schema |
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| Label | Meaning | |
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|-------|---------| |
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| `B-PIPE` | Beginning of pipeline number | |
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| `I-PIPE` | Inside pipeline number | |
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| `O` | Outside (not pipeline number) | |
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### Splits |
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Data was randomly split. |
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| Split | Examples | |
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|-------|----------| |
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| train | 400 | |
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| validation | 50 | |
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| test | 50 | |
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### Data Creation |
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- **Source:** Digitize-PID |
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- **Annotation:** Automatic BIO tagging with character-level alignment |
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## Usage |
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### With Hugging Face Datasets |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("hamzas/digitize-pid-ner") |
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print(dataset) |
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# Access example |
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example = dataset['train'][0] |
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print(example['tokens']) |
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print(example['labels']) |
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``` |
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