Datasets:

SDVM
/

xtreme-PAN-X.de

license: cc-by-sa-4.0
task_categories:
  - token-classification
language:
  - de
tags:
  - ner
  - named-entity-recognition
  - multilingual
  - sdvm
  - refined
pretty_name: SDVM Refined PAN-X.de (XTREME NER)
dataset_info:
  features:
    - name: tokens
      sequence: string
    - name: ner_tags
      sequence:
        class_label:
          names:
            '0': O
            '1': B-PER
            '2': I-PER
            '3': B-ORG
            '4': I-ORG
            '5': B-LOC
            '6': I-LOC
    - name: tokens_refined
      sequence: string
    - name: ner_tags_refined
      sequence:
        class_label:
          names:
            '0': O
            '1': B-PER
            '2': I-PER
            '3': B-ORG
            '4': I-ORG
            '5': B-LOC
            '6': I-LOC
    - name: langs
      sequence: string
  splits:
    - name: train
      num_examples: 20000
    - name: validation
      num_examples: 10000
    - name: test
      num_examples: 10000

SDVM Refined PAN-X.de (XTREME NER)

This is an enhanced version of the PAN-X German NER dataset from the XTREME benchmark (config: PAN-X.de), refined by SDVM (Synthetic Data Vending Machine) to improve token quality for Named Entity Recognition.

Refinement

The original PAN-X.de dataset is derived from WikiANN/Wikipedia and contains various artifacts from the extraction process. SDVM applied automated token refinement that removes Wikipedia markup noise while preserving entity annotations:

Markup token removal: Removed ~8.5% of tokens that were Wikipedia formatting artifacts (bold markers, quote marks, section headers, template brackets, etc.)
Redirect cleanup: Removed WEITERLEITUNG (German Wikipedia redirect markers)
Embedded markup stripping: Cleaned tokens containing partial markup (e.g., Friedrichsaue with trailing brackets)
Tag continuity repair: Automatically fixed B-/I- tag sequences after token removal to maintain valid IOB2 format

Dataset Structure

Each example contains both the original and refined versions:

Column	Description
`tokens`	Original token sequence
`ner_tags`	Original NER tags (IOB2 format)
`tokens_refined`	Cleaned token sequence
`ner_tags_refined`	Adjusted NER tags for refined tokens
`langs`	Language tags

NER Labels

ID	Tag
0	O
1	B-PER
2	I-PER
3	B-ORG
4	I-ORG
5	B-LOC
6	I-LOC

Example

Original: ['**', "''", 'Lou', 'Salome', "''", '.'] with tags [O, O, B-PER, I-PER, O, O]

Refined: ['Lou', 'Salome', '.'] with tags [B-PER, I-PER, O]

Usage

from datasets import load_dataset

# Load the refined dataset
ds = load_dataset("SDVM/xtreme-PAN-X.de")

# Use refined columns for training
train_tokens = ds["train"]["tokens_refined"]
train_tags = ds["train"]["ner_tags_refined"]

# Or use original columns for comparison
orig_tokens = ds["train"]["tokens"]
orig_tags = ds["train"]["ner_tags"]

Source

Original dataset: google/xtreme (PAN-X.de config)
Reference: Chapter 4 of Natural Language Processing with Transformers
Refinement by: SDVM