
# NLU Few-shot Benchmark - English and German
This is a few-shot training dataset from the domain of human-robot interaction.
It contains texts in German and English language with 64 different utterances (classes).
Each utterance (class) has exactly 20 samples in the training set.
This leads to a total of 1280 different training samples.

The dataset is intended to benchmark the intent classifiers of chat bots in English and especially in German language.
We are building on our
[deutsche-telekom/NLU-Evaluation-Data-en-de](https://huggingface.co/datasets/deutsche-telekom/NLU-Evaluation-Data-en-de)
data set

## Creator

This data set was compiled and open sourced by [Philip May](https://may.la/)
of [Deutsche Telekom](https://www.telekom.de/).

## Processing Steps
- drop `NaN` values
- drop duplicates in `answer_de` and `answer`
- delete all rows where `answer_de` has more than 70 characters
- add column `label`: `df["label"] = df["scenario"] + "_" + df["intent"]`
- remove classes (`label`) with less than 25 samples:
  - `audio_volume_other`
  - `cooking_query`
  - `general_greet`
  - `music_dislikeness`
- random selection for train set - exactly 20 samples for each class (`label`)
- rest for test set

## Copyright
Copyright (c) the authors of [xliuhw/NLU-Evaluation-Data](https://github.com/xliuhw/NLU-Evaluation-Data)\
Copyright (c) 2022 [Philip May](https://may.la/), [Deutsche Telekom AG](https://www.telekom.com/)

All data is released under the
[Creative Commons Attribution 4.0 International License (CC BY 4.0)](http://creativecommons.org/licenses/by/4.0/).

task_categories:text-classification task_ids:intent-classification multilinguality:multilingual size_categories:1K<n<10K source_datasets:extended|deutsche-telekom/NLU-Evaluation-Data-en-de language:en language:de license:cc-by-4.0 region:us
{"language": ["en", "de"], "license": "cc-by-4.0", "multilinguality": ["multilingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|deutsche-telekom/NLU-Evaluation-Data-en-de"], "task_categories": ["text-classification"], "task_ids": ["intent-classification"]}
TAGS
#task_categories-text-classification #task_ids-intent-classification #multilinguality-multilingual #size_categories-1K<n<10K #source_datasets-extended|deutsche-telekom/NLU-Evaluation-Data-en-de #language-English #language-German #license-cc-by-4.0 #region-us 


# NLU Few-shot Benchmark - English and German
This is a few-shot training dataset from the domain of human-robot interaction.
It contains texts in German and English language with 64 different utterances (classes).
Each utterance (class) has exactly 20 samples in the training set.
This leads to a total of 1280 different training samples.

The dataset is intended to benchmark the intent classifiers of chat bots in English and especially in German language.
We are building on our
deutsche-telekom/NLU-Evaluation-Data-en-de
data set

## Creator

This data set was compiled and open sourced by Philip May
of Deutsche Telekom.

## Processing Steps
- drop 'NaN' values
- drop duplicates in 'answer_de' and 'answer'
- delete all rows where 'answer_de' has more than 70 characters
- add column 'label': 'df["label"] = df["scenario"] + "_" + df["intent"]'
- remove classes ('label') with less than 25 samples:
  - 'audio_volume_other'
  - 'cooking_query'
  - 'general_greet'
  - 'music_dislikeness'
- random selection for train set - exactly 20 samples for each class ('label')
- rest for test set

## Copyright
Copyright (c) the authors of xliuhw/NLU-Evaluation-Data\
Copyright (c) 2022 Philip May, Deutsche Telekom AG

All data is released under the
Creative Commons Attribution 4.0 International License (CC BY 4.0).

# NLU Few-shot Benchmark - English and German
This is a few-shot training dataset from the domain of human-robot interaction.
It contains texts in German and English language with 64 different utterances (classes).
Each utterance (class) has exactly 20 samples in the training set.
This leads to a total of 1280 different training samples.

The dataset is intended to benchmark the intent classifiers of chat bots in English and especially in German language.
We are building on our
deutsche-telekom/NLU-Evaluation-Data-en-de
data set ## Creator

This data set was compiled and open sourced by Philip May
of Deutsche Telekom. ## Processing Steps
- drop 'NaN' values
- drop duplicates in 'answer_de' and 'answer'
- delete all rows where 'answer_de' has more than 70 characters
- add column 'label': 'df["label"] = df["scenario"] + "_" + df["intent"]'
- remove classes ('label') with less than 25 samples:
  - 'audio_volume_other'
  - 'cooking_query'
  - 'general_greet'
  - 'music_dislikeness'
- random selection for train set - exactly 20 samples for each class ('label')
- rest for test set ## Copyright
Copyright (c) the authors of xliuhw/NLU-Evaluation-Data\
Copyright (c) 2022 Philip May, Deutsche Telekom AG

All data is released under the
Creative Commons Attribution 4.0 International License (CC BY 4.0).
TAGS
#task_categories-text-classification #task_ids-intent-classification #multilinguality-multilingual #size_categories-1K<n<10K #source_datasets-extended|deutsche-telekom/NLU-Evaluation-Data-en-de #language-English #language-German #license-cc-by-4.0 #region-us 
 # NLU Few-shot Benchmark - English and German
This is a few-shot training dataset from the domain of human-robot interaction.
It contains texts in German and English language with 64 different utterances (classes).
Each utterance (class) has exactly 20 samples in the training set.
This leads to a total of 1280 different training samples.

The dataset is intended to benchmark the intent classifiers of chat bots in English and especially in German language.
We are building on our
deutsche-telekom/NLU-Evaluation-Data-en-de
data set ## Creator

This data set was compiled and open sourced by Philip May
of Deutsche Telekom. ## Processing Steps
- drop 'NaN' values
- drop duplicates in 'answer_de' and 'answer'
- delete all rows where 'answer_de' has more than 70 characters
- add column 'label': 'df["label"] = df["scenario"] + "_" + df["intent"]'
- remove classes ('label') with less than 25 samples:
  - 'audio_volume_other'
  - 'cooking_query'
  - 'general_greet'
  - 'music_dislikeness'
- random selection for train set - exactly 20 samples for each class ('label')
- rest for test set ## Copyright
Copyright (c) the authors of xliuhw/NLU-Evaluation-Data\
Copyright (c) 2022 Philip May, Deutsche Telekom AG

All data is released under the
Creative Commons Attribution 4.0 International License (CC BY 4.0).