Error code:   ConfigNamesError
Exception:    KeyError
Message:      'feature'
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                                   ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1031, in dataset_module_factory
                  raise e1 from None
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1004, in dataset_module_factory
                  ).get_module()
                    ^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 605, in get_module
                  dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data)
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/info.py", line 386, in from_dataset_card_data
                  dataset_info = DatasetInfo._from_yaml_dict(dataset_card_data["dataset_info"])
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/info.py", line 317, in _from_yaml_dict
                  yaml_data["features"] = Features._from_yaml_list(yaml_data["features"])
                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2031, in _from_yaml_list
                  return cls.from_dict(from_yaml_inner(yaml_data))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1876, in from_dict
                  obj = generate_from_dict(dic)
                        ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1463, in generate_from_dict
                  return {key: generate_from_dict(value) for key, value in obj.items()}
                               ^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1478, in generate_from_dict
                  feature = obj.pop("feature")
                            ^^^^^^^^^^^^^^^^^^
              KeyError: 'feature'

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GoEmotions (Preprocessed)

Dataset Description

This dataset contains a preprocessed and standardized version of GoEmotions for multi-label emotion classification.
It is designed for seamless use with transformer-based language models and consistent benchmarking alongside other emotion datasets.

The preprocessing ensures unified label representations and removes unnecessary metadata while preserving the original semantic and emotional content.

Supported Tasks

Multi-label emotion classification
Emotion representation learning
Cross-dataset benchmarking

Dataset Structure

The dataset is split into:

train
validation
test

Each split follows the same schema.

Data Format

Each example consists of:

text (string): Preprocessed text input
labels : Multi-one-hot encoded emotion labels (length = 28)

Each label is binary:

1 → emotion present
0 → emotion absent

Multiple emotions may be active for a single sample.

Emotion Label Mapping (28 Classes)

Index	Emotion
0	Admiration
1	Amusement
2	Anger
3	Annoyance
4	Approval
5	Caring
6	Confusion
7	Curiosity
8	Desire
9	Disappointment
10	Disapproval
11	Disgust
12	Embarrassment
13	Excitement
14	Fear
15	Gratitude
16	Grief
17	Joy
18	Love
19	Nervousness
20	Optimism
21	Pride
22	Realization
23	Relief
24	Remorse
25	Sadness
26	Surprise
27	Neutral

Preprocessing Details

The following preprocessing steps were applied:

Conversion to multi-one-hot label encoding
Standardization to a fixed 28-class emotion space
Removal of extraneous metadata
Text normalization
Preprocessing applied before tokenization

Intended Use

This dataset is intended for:

Training and evaluating multi-label emotion classifiers
Transformer-based NLP experiments
Emotion analysis and representation learning

Limitations

The dataset contains preprocessed text only
Raw GoEmotions data is not included
Emotion annotations reflect annotator perception and may contain subjectivity

Citation

If you use this dataset, please cite the original GoEmotions paper:

@inproceedings{demszky2020goemotions,
  title     = {GoEmotions: A Dataset of Fine-Grained Emotions},
  author    = {Demszky, Dorottya and Movshovitz-Attias, Dana and Ko, Jeongwoo and Cowen, Alan and Nemade, Gaurav and Ravi, Sujith},
  booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
  year      = {2020}
}

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