metadata

language:
  - de
license:
  - cc-by-nc-sa-3.0
multilinguality:
  - monolingual
task_categories:
  - text-classification
task_ids:
  - multi-class-classification
  - sentiment-classification
pretty_name: FinancialPhrasebankGerman
tags:
  - finance
dataset_info:
  features:
    - name: sentence
      dtype: string
    - name: label
      dtype:
        class_label:
          names:
            '0': negative
            '1': neutral
            '2': positive
  splits:
    - name: train
      num_bytes: 422345
      num_examples: 2763
    - name: validation
      num_bytes: 51710
      num_examples: 344
    - name: test
      num_bytes: 55109
      num_examples: 346
  download_size: 318382
  dataset_size: 529164
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*

Dataset Card for German financial_phrasebank

Dataset Description

Dataset Summary

This datset is a German translation of the financial phrasebank of Malo et al. (2013) with a minimum agreement rate between annotators of 75% (3453 observations in total). The translation was mechanically accomplished with Deepl.

Supported Tasks and Leaderboards

Sentiment Classification

Languages

German

Dataset Structure

Data Instances

{ "sentence": "Die finnische nationale Fluggesellschaft gab an, dass der Nettoverlust in den Monaten April bis Juni 26 Millionen Euro betrug, verglichen mit einem Nettogewinn von 13 Millionen Euro im Vorjahr..",
  "label": "negative"
}

Data Fields

sentence: a tokenized line from the dataset
label: a label corresponding to the class as a string: 'positive', 'negative' or 'neutral'

Data Splits

The data is splitted in a train, test and validation set using stratified sampling:

train (2763 observations)
validation (344 observations)
test (346 observations)

Further Information

For further information regarding the source data or the annotation process, please look at the original paper or the original dataset.

Licensing Information

This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/. In particular, this license permits the free use of the data for non-commercial purposes.

If you are interested in commercial use of the data, please contact the authors of the original datset for an appropriate license: