| --- |
| 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 |
| 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 |
| config_names: |
| - default |
| - dictionary |
| - ptb |
| --- |
| |
| # 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. |
