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.

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

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, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, 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-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, other

Dataset Card for NSME-COM

Dataset Summary

In this digital age, the E-Commerce industry has increasingly become a vital component of business strategy and development. To streamline, enhance and take the customer experience to the highest level, NLP can help create surprisingly massive value in the E-Commerce industry.

One of the most popular NLP use-cases is a chatbot. With a chatbot you can automate your customer engagement saving yourself time and other resources. Offering an enhanced and simplified customer experience you can increase your sales and also offer your website visitors personalized recommendations. The NSME-COM dataset (NeuralSpace Massive E-Comm) is a manually curated dataset by data engineers at NeuralSpace for the insurance and retail domain. The dataset contains intents (the action users want to execute) and examples (anything that a user sends to the chatbot) that can be used to build a chatbot. The files in this dataset are available in JSON format.

Supported Tasks

nsme-com

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.

nsme-com

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

Data Splits

nsme-com

	train	test
nsme-com	1725	406

Contributions

Ankur Saxena ([email protected])

Downloads last month: 57

Papers with Code

Size of downloaded dataset files:

10.86 KB