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hamzas/digitize-pid-ner

Digitize-PID: Pipeline numbers (NER) Note: I am not the author of this dataset Named Entity Recognition dataset for extracting pipeline numbers from full text of P&ID (Piping and Instrumentation Diagram) documents. Dataset Details Dataset Description Pipeline numbers are structured identifiers in engineering documents: Example Format: A-123-BC (3-5 segments with a separator such as -, , or _) Use case: Automated extraction from P&ID document text… See the full description on the dataset page: https://huggingface.co/datasets/hamzas/digitize-pid-ner.

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Digitize-PID: Pipeline numbers (NER)

Note: I am not the author of this dataset

Named Entity Recognition dataset for extracting pipeline numbers from full text of P&ID (Piping and Instrumentation Diagram) documents.

Dataset Details

Dataset Description

Pipeline numbers are structured identifiers in engineering documents:

—Example Format: A-123-BC (3-5 segments with a separator such as -, , or _)
—Use case: Automated extraction from P&ID document text
—Domain: Process and piping industry

Data Fields

—id: Unique example identifier
—tokens: List of tokenized words/punctuation
—labels: BIO tags for each token
—pipeline_numbers: Ground truth pipeline numbers
—full_text: Original text

Label Schema

Label	Meaning
`B-PIPE`	Beginning of pipeline number
`I-PIPE`	Inside pipeline number
`O`	Outside (not pipeline number)

Splits

Data was randomly split.

Split	Examples
train	400
validation	50
test	50

Data Creation

—Source: Digitize-PID
—Annotation: Automatic BIO tagging with character-level alignment

Usage

With Hugging Face Datasets

python

from datasets import load_dataset

dataset = load_dataset("hamzas/digitize-pid-ner")

print(dataset)

# Access example
example = dataset['train'][0]
print(example['tokens'])
print(example['labels'])