README.md

metadata

annotations_creators:
  - other
language_creators:
  - other
language:
  - en
expert-generated license:
  - cc-by-nc-sa-4.0
multilinguality:
  - monolingual
size_categories:
  - n<1K
source_datasets:
  - original
task_categories:
  - question-answering
  - text-retrieval
  - text2text-generation
  - other
  - translation
  - conversational
task_ids:
  - extractive-qa
  - closed-domain-qa
  - utterance-retrieval
  - document-retrieval
  - closed-domain-qa
  - open-book-qa
  - closed-book-qa
paperswithcode_id: acronym-identification
pretty_name: Massive E-commerce Dataset for Retail and Insurance domain.
train-eval-index:
  - config: nsds
    task: token-classification
    task_id: entity_extraction
    splits:
      train_split: train
      eval_split: test
    col_mapping:
      sentence: text
      label: target
    metrics:
      - type: nsme-com
        name: NSME-COM
        config: nsds
tags:
  - chatbots
  - e-commerce
  - retail
  - insurance
  - consumer
  - consumer goods
configs:
  - nsds

Dataset Card for NSME-COM

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: NeuralSpace Homepage
• Repository: NSME-COM Dataset
• Point of Contact: Ankur Saxena
• Point of Contact: Ayushman Dash
• Size of downloaded dataset files: 10.86 KB

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 (ankursaxena@neuralspace.ai)