Error code:   ConfigNamesError
Exception:    ValueError
Message:      Each config must include `config_name` field with a string name of a config, but got nsds. 
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 79, in compute_config_names_response
                  config_names = get_dataset_config_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1910, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1885, in dataset_module_factory
                  return HubDatasetModuleFactoryWithoutScript(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1236, in get_module
                  metadata_configs = MetadataConfigs.from_dataset_card_data(dataset_card_data)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/metadata.py", line 227, in from_dataset_card_data
                  raise ValueError(
              ValueError: Each config must include `config_name` field with a string name of a config, but got nsds.

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

YAML Metadata Error: "configs[0]" must be of type object

YAML Metadata Warning: The task_categories "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

YAML Metadata Warning: The task_categories "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Dataset Card for NSME-COM

Dataset Summary

NSME-COM, the NeuralSpace Massive E-commerce Dataset is a collection of resources for training, evaluating, and analyzing natural language understanding systems.

Supported Tasks and Leaderboards

The leaderboard for the GLUE benchmark can be found at this address. It comprises the following tasks:

nsds

A manually-curated domain specific dataset by Data Engineers at NeuralSpace for rare E-commerce domains such as Insurance and Retail for NL researchers and practitioners to evaluate state of the art models here in 100+ languages. The dataset files are available in JSON format.

Languages

The language data in NSME-COM is in English (BCP-47 en)

Dataset Structure

Data Instances

Size of downloaded dataset files: 10.86 KB

An example of 'test' looks as follows.

  "text": "is it good to add roadside assistance?",
  "intent": "Add",
  "type": "Test"
 }

An example of 'train' looks as follows.

  "text": "how can I add my spouse as a nominee?",
  "intent": "Add",
  "type": "Train"
 },

Data Fields

The data fields are the same among all splits.

nsds

text: a string feature.
intent: a string feature.
type: a classification label, with possible values including train or test.

Data Splits

nsds

	train	test
nsds	1725	406

Contributions

Ankur Saxena ([email protected])

Downloads last month: 16

Papers with Code

Size of downloaded dataset files:

10.86 KB