|
--- |
|
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 |
|
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-description) |
|
- [Dataset Summary](#dataset-summary) |
|
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
|
- [Languages](#languages) |
|
- [Dataset Structure](#dataset-structure) |
|
- [Data Instances](#data-instances) |
|
- [Data Fields](#data-fields) |
|
- [Data Splits](#data-splits) |
|
- [Dataset Creation](#dataset-creation) |
|
- [Curation Rationale](#curation-rationale) |
|
- [Source Data](#source-data) |
|
- [Annotations](#annotations) |
|
- [Personal and Sensitive Information](#personal-and-sensitive-information) |
|
- [Considerations for Using the Data](#considerations-for-using-the-data) |
|
- [Social Impact of Dataset](#social-impact-of-dataset) |
|
- [Discussion of Biases](#discussion-of-biases) |
|
- [Other Known Limitations](#other-known-limitations) |
|
- [Additional Information](#additional-information) |
|
- [Dataset Curators](#dataset-curators) |
|
- [Licensing Information](#licensing-information) |
|
- [Citation Information](#citation-information) |
|
- [Contributions](#contributions) |
|
## Dataset Description |
|
- **Homepage:** [https://huggingface.co/asaxena1990) |
|
- **Repository:** [https://huggingface.co/datasets/asaxena1990/NSME-COM) |
|
- **Point of Contact:** (Ayushman Dash <ayushman@neuralspace.ai>, Ankur Saxena <ankursaxena@neuralspace.ai>) |
|
- **Size of downloaded dataset files:** 10.86 KB |
|
### 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](https://gluebenchmark.com/). 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 art models at https://www.neuralspace.ai/ 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 |
|
#### nsds |
|
- **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" |
|
}, |
|
``` |
|
|
|
### Contributions |
|
Ankur Saxena (ankursaxena@neuralspace.ai) |
|
|
