---
dataset_info:
  features:
  - name: id
    dtype: int64
  - name: source
    dtype: string
  - name: source_date
    dtype: string
  - name: tokens
    sequence: string
  - name: ner_label
    sequence: int64
  - name: ner_tag
    sequence: string
  - name: nested_ner_label
    sequence: int64
  - name: nested_ner_tag
    sequence: string
  splits:
  - name: train
    num_bytes: 18729899
    num_examples: 24002
  - name: validation
    num_bytes: 1721290
    num_examples: 2200
  - name: test
    num_bytes: 3993690
    num_examples: 5100
  download_size: 4900445
  dataset_size: 24444879
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
license: mit
task_categories:
- token-classification
language:
- de
tags:
- GermEval
pretty_name: GermEval 2014 NER challenge dataset
size_categories:
- 1M<n<10M
---

# GermEval 14 NER dataset

This dataset includes the actual NER tags (B-PER, B-LOC, etc.) besides the labels (0, 1, 2, ...) and requires no code execution when loading. Structured as follow

```
DatasetDict({
    train: Dataset({
        features: ['id', 'source', 'source_date', 'tokens', 'ner_label', 'ner_tag', 'nested_ner_label', 'nested_ner_tag'],
        num_rows: 24002
    })
    validation: Dataset({
        features: ['id', 'source', 'source_date', 'tokens', 'ner_label', 'ner_tag', 'nested_ner_label', 'nested_ner_tag'],
        num_rows: 2200
    })
    test: Dataset({
        features: ['id', 'source', 'source_date', 'tokens', 'ner_label', 'ner_tag', 'nested_ner_label', 'nested_ner_tag'],
        num_rows: 5100
    })
})

```

# Citation
Based on the data from the GermEval14 NER challenge, please cite the original authors when using this dataset:

```
@article{benikovaGermEval2014Named,
  title = {{{GermEval}} 2014 {{Named Entity Recognition Shared Task}}: {{Companion Paper}}},
  author = {Benikova, Darina and Biemann, Chris and Kisselew, Max and Pado, Sebastian},
  abstract = {This paper describes the GermEval 2014 Named Entity Recognition (NER) Shared Task workshop at KONVENS. It provides background information on the motivation of this task, the data-set, the evaluation method, and an overview of the participating systems, followed by a discussion of their results. In contrast to previous NER tasks, the GermEval 2014 edition uses an extended tagset to account for derivatives of names and tokens that contain name parts. Further, nested named entities had to be predicted, i.e. names that contain other names. The eleven participating teams employed a wide range of techniques in their systems. The most successful systems used state-of-theart machine learning methods, combined with some knowledge-based features in hybrid systems.},
  langid = {english},
}
```