
# Dataset Card for `reviews_with_drift`

## Table of Contents
- [Table of Contents](#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

### Dataset Summary

This dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added (`age`, `gender`, `context`) as well as a made up timestamp `prediction_ts` of when the inference took place.

### Supported Tasks and Leaderboards

`text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).

### Languages

Text is mainly written in english.

## Dataset Structure

### Data Instances

#### default

An example of `training` looks as follows:

```json
{
 'prediction_ts': 1650092416.0,
 'age': 44,
 'gender': 'female',
 'context': 'movies',
 'text': "An interesting premise, and Billy Drago is always good as a dangerous nut-bag (side note: I'd love to see Drago, Stephen McHattie and Lance Hendrikson in a flick together; talk about raging cheekbones!). The soundtrack wasn't terrible, either.<br /><br />But the acting--even that of such professionals as Drago and Debbie Rochon--was terrible, the directing worse (perhaps contributory to the former), the dialog chimp-like, and the camera work, barely tolerable. Still, it was the SETS that got a big 10 on my oy-vey scale. I don't know where this was filmed, but were I to hazard a guess, it would be either an open-air museum, or one of those re-enactment villages, where everything is just a bit too well-kept to do more than suggest the real Old West. Okay, so it was shot on a college kid's budget. That said, I could have forgiven one or two of the aforementioned faults. But taken all together, and being generous, I could not see giving it more than three stars.",
 'label': 0
 }
```

### Data Fields

#### default

The data fields are the same among all splits. An example of `training` looks as follows:

- `prediction_ts`: a `float` feature.
- `age`: an `int` feature.
- `gender`: a `string` feature.
- `context`: a `string` feature.
- `text`: a `string` feature.
- `label`: a `ClassLabel` feature, with possible values including negative(0) and positive(1).

### Data Splits

|   name   |training|validation|production |
|----------|-------:|---------:|----------:|
|  default |  9916  |   2479   |   40079   |

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Contributions

Thanks to [@fjcasti1](https://github.com/fjcasti1) for adding this dataset.
task_categories:text-classification task_ids:sentiment-classification annotations_creators:expert-generated language_creators:expert-generated multilinguality:monolingual size_categories:10K<n<100K source_datasets:extended|imdb language:en license:mit region:us
{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|imdb"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "sentiment-classification-reviews-with-drift"}
TAGS
#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|imdb #language-English #license-mit #region-us 

Dataset Card for 'reviews\_with\_drift'
=======================================


Table of Contents
-----------------


* Table of Contents
* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ 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
-------------------


### Dataset Summary


This dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added ('age', 'gender', 'context') as well as a made up timestamp 'prediction\_ts' of when the inference took place.


### Supported Tasks and Leaderboards


'text-classification', 'sentiment-classification': The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).


### Languages


Text is mainly written in english.


Dataset Structure
-----------------


### Data Instances


#### default


An example of 'training' looks as follows:


### Data Fields


#### default


The data fields are the same among all splits. An example of 'training' looks as follows:


* 'prediction\_ts': a 'float' feature.
* 'age': an 'int' feature.
* 'gender': a 'string' feature.
* 'context': a 'string' feature.
* 'text': a 'string' feature.
* 'label': a 'ClassLabel' feature, with possible values including negative(0) and positive(1).


### Data Splits



Dataset Creation
----------------


### Curation Rationale


### Contributions


Thanks to @fjcasti1 for adding this dataset.



### Dataset Summary


This dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added ('age', 'gender', 'context') as well as a made up timestamp 'prediction\_ts' of when the inference took place. ### Supported Tasks and Leaderboards


'text-classification', 'sentiment-classification': The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative). ### Languages


Text is mainly written in english.


Dataset Structure
----------------- ### Data Instances #### default


An example of 'training' looks as follows: ### Data Fields #### default


The data fields are the same among all splits. An example of 'training' looks as follows:


* 'prediction\_ts': a 'float' feature.
* 'age': an 'int' feature.
* 'gender': a 'string' feature.
* 'context': a 'string' feature.
* 'text': a 'string' feature.
* 'label': a 'ClassLabel' feature, with possible values including negative(0) and positive(1). ### Data Splits



Dataset Creation
---------------- ### Curation Rationale ### Contributions


Thanks to @fjcasti1 for adding this dataset.
TAGS
#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|imdb #language-English #license-mit #region-us 
 ### Dataset Summary


This dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added ('age', 'gender', 'context') as well as a made up timestamp 'prediction\_ts' of when the inference took place. ### Supported Tasks and Leaderboards


'text-classification', 'sentiment-classification': The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative). ### Languages


Text is mainly written in english.


Dataset Structure
----------------- ### Data Instances #### default


An example of 'training' looks as follows: ### Data Fields #### default


The data fields are the same among all splits. An example of 'training' looks as follows:


* 'prediction\_ts': a 'float' feature.
* 'age': an 'int' feature.
* 'gender': a 'string' feature.
* 'context': a 'string' feature.
* 'text': a 'string' feature.
* 'label': a 'ClassLabel' feature, with possible values including negative(0) and positive(1). ### Data Splits



Dataset Creation
---------------- ### Curation Rationale ### Contributions


Thanks to @fjcasti1 for adding this dataset.
passage: TAGS
#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|imdb #language-English #license-mit #region-us 
### Dataset Summary


This dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added ('age', 'gender', 'context') as well as a made up timestamp 'prediction\_ts' of when the inference took place.### Supported Tasks and Leaderboards


'text-classification', 'sentiment-classification': The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).### Languages


Text is mainly written in english.


Dataset Structure
-----------------### Data Instances#### default


An example of 'training' looks as follows:### Data Fields#### default


The data fields are the same among all splits. An example of 'training' looks as follows:


* 'prediction\_ts': a 'float' feature.
* 'age': an 'int' feature.
* 'gender': a 'string' feature.
* 'context': a 'string' feature.
* 'text': a 'string' feature.
* 'label': a 'ClassLabel' feature, with possible values including negative(0) and positive(1).### Data Splits



Dataset Creation
----------------### Curation Rationale### Contributions


Thanks to @fjcasti1 for adding this dataset.