
# Dataset Card for sst

## 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://nlp.stanford.edu/sentiment/index.html
- **Repository:** [Needs More Information]
- **Paper:** [Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank](https://www.aclweb.org/anthology/D13-1170/)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]

### Dataset Summary

The Stanford Sentiment Treebank is the first corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language.

### Supported Tasks and Leaderboards

- `sentiment-scoring`: Each complete sentence is annotated with a `float` label that indicates its level of positive sentiment from 0.0 to 1.0. One can decide to use only complete sentences or to include the contributions of the sub-sentences (aka phrases). The labels for each phrase are included in the `dictionary` configuration. To obtain all the phrases in a sentence we need to visit the parse tree included with each example. In contrast, the `ptb` configuration explicitly provides all the labelled parse trees in Penn Treebank format. Here the labels are binned in 5 bins from 0 to 4.
- `sentiment-classification`: We can transform the above into a binary sentiment classification task by rounding each label to 0 or 1.

### Languages

The text in the dataset is in English

## Dataset Structure

### Data Instances

For the `default` configuration:
```
{'label': 0.7222200036048889,
 'sentence': 'Yet the act is still charming here .',
 'tokens': 'Yet|the|act|is|still|charming|here|.',
 'tree': '15|13|13|10|9|9|11|12|10|11|12|14|14|15|0'}
```

For the `dictionary` configuration:
```
{'label': 0.7361099720001221, 
'phrase': 'still charming'}
```

For the `ptb` configuration:
```
{'ptb_tree': '(3 (2 Yet) (3 (2 (2 the) (2 act)) (3 (4 (3 (2 is) (3 (2 still) (4 charming))) (2 here)) (2 .))))'}
```

### Data Fields

- `sentence`: a complete sentence expressing an opinion about a film
- `label`: the degree of "positivity" of the opinion, on a scale between 0.0 and 1.0
- `tokens`: a sequence of tokens that form a sentence
- `tree`: a sentence parse tree formatted as a parent pointer tree
- `phrase`: a sub-sentence of a complete sentence
- `ptb_tree`: a sentence parse tree formatted in Penn Treebank-style, where each component's degree of positive sentiment is labelled on a scale from 0 to 4

### Data Splits

The set of complete sentences (both `default` and `ptb` configurations) is split into a training, validation and test set. The `dictionary` configuration has only one split as it is used for reference rather than for learning.

## Dataset Creation

### Curation Rationale

[Needs More Information]

### Source Data

#### Initial Data Collection and Normalization

[Needs More Information]

#### Who are the source language producers?

Rotten Tomatoes reviewers.

### Annotations

#### Annotation process

[Needs More Information]

#### Who are the annotators?

[Needs More Information]

### Personal and Sensitive Information

[Needs More Information]

## Considerations for Using the Data

### Social Impact of Dataset

[Needs More Information]

### Discussion of Biases

[Needs More Information]

### Other Known Limitations

[Needs More Information]

## Additional Information

### Dataset Curators

[Needs More Information]

### Licensing Information

[Needs More Information]

### Citation Information

```
@inproceedings{socher-etal-2013-recursive,
    title = "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank",
    author = "Socher, Richard  and
      Perelygin, Alex  and
      Wu, Jean  and
      Chuang, Jason  and
      Manning, Christopher D.  and
      Ng, Andrew  and
      Potts, Christopher",
    booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
    month = oct,
    year = "2013",
    address = "Seattle, Washington, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D13-1170",
    pages = "1631--1642",
}
```

### Contributions

Thanks to [@patpizio](https://github.com/patpizio) for adding this dataset.
task_categories:text-classification task_ids:text-scoring task_ids:sentiment-classification task_ids:sentiment-scoring annotations_creators:crowdsourced language_creators:found multilinguality:monolingual size_categories:100K<n<1M size_categories:10K<n<100K source_datasets:original language:en license:unknown region:us
{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M", "10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["text-scoring", "sentiment-classification", "sentiment-scoring"], "paperswithcode_id": "sst", "pretty_name": "Stanford Sentiment Treebank", "config_names": ["default", "dictionary", "ptb"], "dataset_info": [{"config_name": "default", "features": [{"name": "sentence", "dtype": "string"}, {"name": "label", "dtype": "float32"}, {"name": "tokens", "dtype": "string"}, {"name": "tree", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2818768, "num_examples": 8544}, {"name": "validation", "num_bytes": 366205, "num_examples": 1101}, {"name": "test", "num_bytes": 730154, "num_examples": 2210}], "download_size": 7162356, "dataset_size": 3915127}, {"config_name": "dictionary", "features": [{"name": "phrase", "dtype": "string"}, {"name": "label", "dtype": "float32"}], "splits": [{"name": "dictionary", "num_bytes": 12121843, "num_examples": 239232}], "download_size": 7162356, "dataset_size": 12121843}, {"config_name": "ptb", "features": [{"name": "ptb_tree", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2185694, "num_examples": 8544}, {"name": "validation", "num_bytes": 284132, "num_examples": 1101}, {"name": "test", "num_bytes": 566248, "num_examples": 2210}], "download_size": 7162356, "dataset_size": 3036074}]}
TAGS
#task_categories-text-classification #task_ids-text-scoring #task_ids-sentiment-classification #task_ids-sentiment-scoring #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #region-us 


# Dataset Card for sst

## 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

- Homepage: URL
- Repository: 
- Paper: Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
- Leaderboard: 
- Point of Contact: 

### Dataset Summary

The Stanford Sentiment Treebank is the first corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language.

### Supported Tasks and Leaderboards

- 'sentiment-scoring': Each complete sentence is annotated with a 'float' label that indicates its level of positive sentiment from 0.0 to 1.0. One can decide to use only complete sentences or to include the contributions of the sub-sentences (aka phrases). The labels for each phrase are included in the 'dictionary' configuration. To obtain all the phrases in a sentence we need to visit the parse tree included with each example. In contrast, the 'ptb' configuration explicitly provides all the labelled parse trees in Penn Treebank format. Here the labels are binned in 5 bins from 0 to 4.
- 'sentiment-classification': We can transform the above into a binary sentiment classification task by rounding each label to 0 or 1.

### Languages

The text in the dataset is in English

## Dataset Structure

### Data Instances

For the 'default' configuration:


For the 'dictionary' configuration:


For the 'ptb' configuration:


### Data Fields

- 'sentence': a complete sentence expressing an opinion about a film
- 'label': the degree of "positivity" of the opinion, on a scale between 0.0 and 1.0
- 'tokens': a sequence of tokens that form a sentence
- 'tree': a sentence parse tree formatted as a parent pointer tree
- 'phrase': a sub-sentence of a complete sentence
- 'ptb_tree': a sentence parse tree formatted in Penn Treebank-style, where each component's degree of positive sentiment is labelled on a scale from 0 to 4

### Data Splits

The set of complete sentences (both 'default' and 'ptb' configurations) is split into a training, validation and test set. The 'dictionary' configuration has only one split as it is used for reference rather than for learning.

## Dataset Creation

### Curation Rationale



### Source Data

#### Initial Data Collection and Normalization



#### Who are the source language producers?

Rotten Tomatoes reviewers.

### Annotations

#### Annotation process



#### Who are the annotators?



### 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







### Contributions

Thanks to @patpizio for adding this dataset.
# Dataset Card for sst ## 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

- Homepage: URL
- Repository: 
- Paper: Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
- Leaderboard: 
- Point of Contact: ### Dataset Summary

The Stanford Sentiment Treebank is the first corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language. ### Supported Tasks and Leaderboards

- 'sentiment-scoring': Each complete sentence is annotated with a 'float' label that indicates its level of positive sentiment from 0.0 to 1.0. One can decide to use only complete sentences or to include the contributions of the sub-sentences (aka phrases). The labels for each phrase are included in the 'dictionary' configuration. To obtain all the phrases in a sentence we need to visit the parse tree included with each example. In contrast, the 'ptb' configuration explicitly provides all the labelled parse trees in Penn Treebank format. Here the labels are binned in 5 bins from 0 to 4.
- 'sentiment-classification': We can transform the above into a binary sentiment classification task by rounding each label to 0 or 1. ### Languages

The text in the dataset is in English ## Dataset Structure ### Data Instances

For the 'default' configuration:


For the 'dictionary' configuration:


For the 'ptb' configuration: ### Data Fields

- 'sentence': a complete sentence expressing an opinion about a film
- 'label': the degree of "positivity" of the opinion, on a scale between 0.0 and 1.0
- 'tokens': a sequence of tokens that form a sentence
- 'tree': a sentence parse tree formatted as a parent pointer tree
- 'phrase': a sub-sentence of a complete sentence
- 'ptb_tree': a sentence parse tree formatted in Penn Treebank-style, where each component's degree of positive sentiment is labelled on a scale from 0 to 4 ### Data Splits

The set of complete sentences (both 'default' and 'ptb' configurations) is split into a training, validation and test set. The 'dictionary' configuration has only one split as it is used for reference rather than for learning. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers?

Rotten Tomatoes reviewers. ### Annotations #### Annotation process #### Who are the annotators? ### 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 ### Contributions

Thanks to @patpizio for adding this dataset.
TAGS
#task_categories-text-classification #task_ids-text-scoring #task_ids-sentiment-classification #task_ids-sentiment-scoring #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #region-us 
 # Dataset Card for sst ## 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

- Homepage: URL
- Repository: 
- Paper: Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
- Leaderboard: 
- Point of Contact: ### Dataset Summary

The Stanford Sentiment Treebank is the first corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language. ### Supported Tasks and Leaderboards

- 'sentiment-scoring': Each complete sentence is annotated with a 'float' label that indicates its level of positive sentiment from 0.0 to 1.0. One can decide to use only complete sentences or to include the contributions of the sub-sentences (aka phrases). The labels for each phrase are included in the 'dictionary' configuration. To obtain all the phrases in a sentence we need to visit the parse tree included with each example. In contrast, the 'ptb' configuration explicitly provides all the labelled parse trees in Penn Treebank format. Here the labels are binned in 5 bins from 0 to 4.
- 'sentiment-classification': We can transform the above into a binary sentiment classification task by rounding each label to 0 or 1. ### Languages

The text in the dataset is in English ## Dataset Structure ### Data Instances

For the 'default' configuration:


For the 'dictionary' configuration:


For the 'ptb' configuration: ### Data Fields

- 'sentence': a complete sentence expressing an opinion about a film
- 'label': the degree of "positivity" of the opinion, on a scale between 0.0 and 1.0
- 'tokens': a sequence of tokens that form a sentence
- 'tree': a sentence parse tree formatted as a parent pointer tree
- 'phrase': a sub-sentence of a complete sentence
- 'ptb_tree': a sentence parse tree formatted in Penn Treebank-style, where each component's degree of positive sentiment is labelled on a scale from 0 to 4 ### Data Splits

The set of complete sentences (both 'default' and 'ptb' configurations) is split into a training, validation and test set. The 'dictionary' configuration has only one split as it is used for reference rather than for learning. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers?

Rotten Tomatoes reviewers. ### Annotations #### Annotation process #### Who are the annotators? ### 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 ### Contributions

Thanks to @patpizio for adding this dataset.