Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
License:
| annotations_creators: | |
| - found | |
| language_creators: | |
| - found | |
| language: | |
| - en | |
| license: | |
| - unknown | |
| multilinguality: | |
| - monolingual | |
| size_categories: | |
| - 10K<n<100K | |
| source_datasets: | |
| - extended|other-tweet-datasets | |
| task_categories: | |
| - text-classification | |
| task_ids: | |
| - multi-class-classification | |
| - sentiment-classification | |
| paperswithcode_id: tweeteval | |
| pretty_name: TweetEval | |
| config_names: | |
| - sentiment | |
| dataset_info: | |
| - config_name: sentiment | |
| features: | |
| - name: text | |
| dtype: string | |
| - name: label | |
| dtype: | |
| class_label: | |
| names: | |
| '0': negative | |
| '1': neutral | |
| '2': positive | |
| splits: | |
| - name: train | |
| num_bytes: 5425122 | |
| num_examples: 45615 | |
| - name: test | |
| num_bytes: 1279540 | |
| num_examples: 12284 | |
| - name: validation | |
| num_bytes: 239084 | |
| num_examples: 2000 | |
| download_size: 4849675 | |
| dataset_size: 6943746 | |
| configs: | |
| - config_name: sentiment | |
| data_files: | |
| - split: train | |
| path: sentiment/train-* | |
| - split: test | |
| path: sentiment/test-* | |
| - split: validation | |
| path: sentiment/validation-* | |
| train-eval-index: | |
| - config: sentiment | |
| task: text-classification | |
| task_id: multi_class_classification | |
| splits: | |
| train_split: train | |
| eval_split: test | |
| col_mapping: | |
| text: text | |
| label: target | |
| metrics: | |
| - type: accuracy | |
| name: Accuracy | |
| - type: f1 | |
| name: F1 macro | |
| args: | |
| average: macro | |
| - type: f1 | |
| name: F1 micro | |
| args: | |
| average: micro | |
| - type: f1 | |
| name: F1 weighted | |
| args: | |
| average: weighted | |
| - type: precision | |
| name: Precision macro | |
| args: | |
| average: macro | |
| - type: precision | |
| name: Precision micro | |
| args: | |
| average: micro | |
| - type: precision | |
| name: Precision weighted | |
| args: | |
| average: weighted | |
| - type: recall | |
| name: Recall macro | |
| args: | |
| average: macro | |
| - type: recall | |
| name: Recall micro | |
| args: | |
| average: micro | |
| - type: recall | |
| name: Recall weighted | |
| args: | |
| average: weighted | |
| # Dataset Card for tweet_eval | |
| ## 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:** [Needs More Information] | |
| - **Repository:** [GitHub](https://github.com/cardiffnlp/tweeteval) | |
| - **Paper:** [EMNLP Paper](https://arxiv.org/pdf/2010.12421.pdf) | |
| - **Leaderboard:** [GitHub Leaderboard](https://github.com/cardiffnlp/tweeteval) | |
| - **Point of Contact:** [Needs More Information] | |
| ### Dataset Summary | |
| TweetEval consists of seven heterogenous tasks in Twitter, all framed as multi-class tweet classification. This configuration exposes the **sentiment** task only. All datasets have been unified into the same benchmark, with each dataset presented in the same format and with fixed training, validation and test splits. | |
| ### Supported Tasks and Leaderboards | |
| - `text_classification`: The dataset can be trained using a SentenceClassification model from HuggingFace transformers. | |
| ### Languages | |
| The text in the dataset is in English, as spoken by Twitter users. | |
| ## Dataset Structure | |
| ### Data Instances | |
| An instance from `sentiment` config: | |
| ``` | |
| {'label': 2, 'text': '"QT @user In the original draft of the 7th book, Remus Lupin survived the Battle of Hogwarts. #HappyBirthdayRemusLupin"'} | |
| ``` | |
| ### Data Fields | |
| For `sentiment` config: | |
| - `text`: a `string` feature containing the tweet. | |
| - `label`: an `int` classification label with the following mapping: | |
| `0`: negative | |
| `1`: neutral | |
| `2`: positive | |
| ### Data Splits | |
| | name | train | validation | test | | |
| | --------- | ----- | ---------- | ----- | | |
| | sentiment | 45615 | 2000 | 12284 | | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| [Needs More Information] | |
| ### Source Data | |
| #### Initial Data Collection and Normalization | |
| [Needs More Information] | |
| #### Who are the source language producers? | |
| [Needs More Information] | |
| ### 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 | |
| Francesco Barbieri, Jose Camacho-Collados, Luis Espiinosa-Anke and Leonardo Neves through Cardiff NLP. | |
| ### Licensing Information | |
| This dataset requires complying with Twitter [Terms Of Service](https://twitter.com/tos) and Twitter API [Terms Of Service](https://developer.twitter.com/en/developer-terms/agreement-and-policy) | |
| Sentiment license: [Creative Commons Attribution 3.0 Unported License](https://groups.google.com/g/semevaltweet/c/k5DDcvVb_Vo/m/zEOdECFyBQAJ) | |
| ### Citation Information | |
| ``` | |
| @inproceedings{barbieri2020tweeteval, | |
| title={{TweetEval:Unified Benchmark and Comparative Evaluation for Tweet Classification}}, | |
| author={Barbieri, Francesco and Camacho-Collados, Jose and Espinosa-Anke, Luis and Neves, Leonardo}, | |
| booktitle={Proceedings of Findings of EMNLP}, | |
| year={2020} | |
| } | |
| ``` | |
| #### Sentiment Analysis: | |
| ``` | |
| @inproceedings{rosenthal2017semeval, | |
| title={SemEval-2017 task 4: Sentiment analysis in Twitter}, | |
| author={Rosenthal, Sara and Farra, Noura and Nakov, Preslav}, | |
| booktitle={Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017)}, | |
| pages={502--518}, | |
| year={2017} | |
| } | |
| ``` | |
| ### Contributions | |
| Thanks to [@gchhablani](https://github.com/gchhablani) and [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. | |