Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
csv
Sub-tasks:
intent-classification
Size:
10K - 100K
License:
deascribe Processing Steps
Browse files
README.md
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## Copyright
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Copyright (c) the authors of [xliuhw/NLU-Evaluation-Data](https://github.com/xliuhw/NLU-Evaluation-Data)\
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Copyright (c) 2022 [Philip May](https://may.la/), [Deutsche Telekom AG](https://www.telekom.com/)
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# NLU Few-shot Benchmark - English and German
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## Processing Steps
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- drop `NaN` values
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- drop duplicates in `answer_de`
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- add `char_count` column
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- delete all rows with `char_count` > 70
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- add column `label`: `df["label"] = df["scenario"] + "_" + df["intent"]`
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- remove classes (`label`) with less than 25 samples:
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- `audio_volume_other`
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- `cooking_query`
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- `general_greet`
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- `music_dislikeness`
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- random selection for train set - exactly 20 samples for each class (`label`)
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- rest for test set
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## Copyright
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Copyright (c) the authors of [xliuhw/NLU-Evaluation-Data](https://github.com/xliuhw/NLU-Evaluation-Data)\
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Copyright (c) 2022 [Philip May](https://may.la/), [Deutsche Telekom AG](https://www.telekom.com/)
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