Datasets:

hamzas
/

digitize-pid-ner

paper: arxiv:2109.03794
size_categories:
  - n<1K
task_categories:
  - token-classification
language:
  - en
tags:
  - pipeline-numbers
  - ner
  - p-and-id

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

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'])