
# Dataset Card for "cqadupstack"

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
- [Additional Information](#additional-information)
  - [Licensing Information](#licensing-information)

## Dataset Description

- **Homepage:** [https://sites.google.com/view/fiqa/?pli=1](https://sites.google.com/view/fiqa/?pli=1)


### Dataset Summary
This is a preprocessed version of fiqa, to make it easily consumable via huggingface. The original dataset can be found [here](https://sites.google.com/view/fiqa/?pli=1).


The growing maturity of Natural Language Processing (NLP) techniques and resources is drastically changing the landscape of many application domains which are dependent on the analysis of unstructured data at scale. The financial domain, with its dependency on the interpretation of multiple unstructured and structured data sources and with its demand for fast and comprehensive decision making is already emerging as a primary ground for the experimentation of NLP, Web Mining and Information Retrieval (IR) techniques. This challenge focuses on advancing the state-of-the-art of aspect-based sentiment analysis and opinion-based Question Answering for the financial domain. 



## Dataset Structure

### Data Instances

An example of 'train' looks as follows.

```json
{
    "question": "How does a 2 year treasury note work?",
    "answer": "Notes and Bonds sell at par (1.0). When rates go up, their value goes down. When rates go down, their value goes up. ..."
}
```

### Data Fields

The data fields are the same among all splits.

- `question`: a `string` feature.
- `answer`: a `string` feature.


## Additional Information

### Licensing Information

This dataset is distributed under the [CC BY-NC](https://creativecommons.org/licenses/by-nc/3.0/) licence providing free access for non-commercial and academic usage.
task_categories:feature-extraction task_categories:sentence-similarity size_categories:10K<n<100K language:en license:cc-by-3.0 region:us
{"language": ["en"], "license": "cc-by-3.0", "size_categories": ["10K<n<100K"], "task_categories": ["feature-extraction", "sentence-similarity"]}
TAGS
#task_categories-feature-extraction #task_categories-sentence-similarity #size_categories-10K<n<100K #language-English #license-cc-by-3.0 #region-us 


# Dataset Card for "cqadupstack"

## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
- Dataset Structure
  - Data Instances
  - Data Fields
- Additional Information
  - Licensing Information

## Dataset Description

- Homepage: URL


### Dataset Summary
This is a preprocessed version of fiqa, to make it easily consumable via huggingface. The original dataset can be found here.


The growing maturity of Natural Language Processing (NLP) techniques and resources is drastically changing the landscape of many application domains which are dependent on the analysis of unstructured data at scale. The financial domain, with its dependency on the interpretation of multiple unstructured and structured data sources and with its demand for fast and comprehensive decision making is already emerging as a primary ground for the experimentation of NLP, Web Mining and Information Retrieval (IR) techniques. This challenge focuses on advancing the state-of-the-art of aspect-based sentiment analysis and opinion-based Question Answering for the financial domain. 



## Dataset Structure

### Data Instances

An example of 'train' looks as follows.



### Data Fields

The data fields are the same among all splits.

- 'question': a 'string' feature.
- 'answer': a 'string' feature.


## Additional Information

### Licensing Information

This dataset is distributed under the CC BY-NC licence providing free access for non-commercial and academic usage.
# Dataset Card for "cqadupstack" ## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
- Dataset Structure
  - Data Instances
  - Data Fields
- Additional Information
  - Licensing Information ## Dataset Description

- Homepage: URL ### Dataset Summary
This is a preprocessed version of fiqa, to make it easily consumable via huggingface. The original dataset can be found here.


The growing maturity of Natural Language Processing (NLP) techniques and resources is drastically changing the landscape of many application domains which are dependent on the analysis of unstructured data at scale. The financial domain, with its dependency on the interpretation of multiple unstructured and structured data sources and with its demand for fast and comprehensive decision making is already emerging as a primary ground for the experimentation of NLP, Web Mining and Information Retrieval (IR) techniques. This challenge focuses on advancing the state-of-the-art of aspect-based sentiment analysis and opinion-based Question Answering for the financial domain. ## Dataset Structure ### Data Instances

An example of 'train' looks as follows. ### Data Fields

The data fields are the same among all splits.

- 'question': a 'string' feature.
- 'answer': a 'string' feature. ## Additional Information ### Licensing Information

This dataset is distributed under the CC BY-NC licence providing free access for non-commercial and academic usage.
TAGS
#task_categories-feature-extraction #task_categories-sentence-similarity #size_categories-10K<n<100K #language-English #license-cc-by-3.0 #region-us 
 # Dataset Card for "cqadupstack" ## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
- Dataset Structure
  - Data Instances
  - Data Fields
- Additional Information
  - Licensing Information ## Dataset Description

- Homepage: URL ### Dataset Summary
This is a preprocessed version of fiqa, to make it easily consumable via huggingface. The original dataset can be found here.


The growing maturity of Natural Language Processing (NLP) techniques and resources is drastically changing the landscape of many application domains which are dependent on the analysis of unstructured data at scale. The financial domain, with its dependency on the interpretation of multiple unstructured and structured data sources and with its demand for fast and comprehensive decision making is already emerging as a primary ground for the experimentation of NLP, Web Mining and Information Retrieval (IR) techniques. This challenge focuses on advancing the state-of-the-art of aspect-based sentiment analysis and opinion-based Question Answering for the financial domain. ## Dataset Structure ### Data Instances

An example of 'train' looks as follows. ### Data Fields

The data fields are the same among all splits.

- 'question': a 'string' feature.
- 'answer': a 'string' feature. ## Additional Information ### Licensing Information

This dataset is distributed under the CC BY-NC licence providing free access for non-commercial and academic usage.
passage: TAGS
#task_categories-feature-extraction #task_categories-sentence-similarity #size_categories-10K<n<100K #language-English #license-cc-by-3.0 #region-us 
# Dataset Card for "cqadupstack"## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
- Dataset Structure
  - Data Instances
  - Data Fields
- Additional Information
  - Licensing Information## Dataset Description

- Homepage: URL### Dataset Summary
This is a preprocessed version of fiqa, to make it easily consumable via huggingface. The original dataset can be found here.


The growing maturity of Natural Language Processing (NLP) techniques and resources is drastically changing the landscape of many application domains which are dependent on the analysis of unstructured data at scale. The financial domain, with its dependency on the interpretation of multiple unstructured and structured data sources and with its demand for fast and comprehensive decision making is already emerging as a primary ground for the experimentation of NLP, Web Mining and Information Retrieval (IR) techniques. This challenge focuses on advancing the state-of-the-art of aspect-based sentiment analysis and opinion-based Question Answering for the financial domain.## Dataset Structure### Data Instances

An example of 'train' looks as follows.### Data Fields

The data fields are the same among all splits.

- 'question': a 'string' feature.
- 'answer': a 'string' feature.## Additional Information### Licensing Information

This dataset is distributed under the CC BY-NC licence providing free access for non-commercial and academic usage.