|
--- |
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annotations_creators: |
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- no-annotation |
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language: |
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- en |
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- es |
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- pt |
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- ja |
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- ar |
|
- in |
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- ko |
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- tr |
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- fr |
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- tl |
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- ru |
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- it |
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- th |
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- de |
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- hi |
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- pl |
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- nl |
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- fa |
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- et |
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- ht |
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- ur |
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- sv |
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- ca |
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- el |
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- fi |
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- cs |
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- iw |
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- da |
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- vi |
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- zh |
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- ta |
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- ro |
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- no |
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- uk |
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- cy |
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- ne |
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- hu |
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- eu |
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- sl |
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- lv |
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- lt |
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- bn |
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- sr |
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- bg |
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- mr |
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- ml |
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- is |
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- te |
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- gu |
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- kn |
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- ps |
|
- ckb |
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- si |
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- hy |
|
- or |
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- pa |
|
- am |
|
- sd |
|
- my |
|
- ka |
|
- km |
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- dv |
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- lo |
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- ug |
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- bo |
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language_creators: |
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- found |
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license: |
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- mit |
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multilinguality: |
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- multilingual |
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pretty_name: Bernice Pretrain Data |
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size_categories: |
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- 1B<n<10B |
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source_datasets: |
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- original |
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tags: |
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- twitter |
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- slang |
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- code switch |
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- social |
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- social media |
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task_categories: |
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- other |
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task_ids: [] |
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--- |
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# Dataset Card for Bernice Pre-train Data |
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## Table of Contents |
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- [Table of Contents](#table-of-contents) |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
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- [Languages](#languages) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Instances](#data-instances) |
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- [Data Fields](#data-fields) |
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- [Data Splits](#data-splits) |
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- [Dataset Creation](#dataset-creation) |
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- [Curation Rationale](#curation-rationale) |
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- [Source Data](#source-data) |
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- [Annotations](#annotations) |
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- [Personal and Sensitive Information](#personal-and-sensitive-information) |
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- [Considerations for Using the Data](#considerations-for-using-the-data) |
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- [Social Impact of Dataset](#social-impact-of-dataset) |
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- [Discussion of Biases](#discussion-of-biases) |
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- [Other Known Limitations](#other-known-limitations) |
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- [Additional Information](#additional-information) |
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- [Dataset Curators](#dataset-curators) |
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- [Licensing Information](#licensing-information) |
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- [Citation Information](#citation-information) |
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- [Contributions](#contributions) |
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## Dataset Description |
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- **Homepage:** N/A |
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- **Repository:** https://github.com/JHU-CLSP/Bernice-Twitter-encoder |
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- **Paper:** _Bernice: A Multilingual Pre-trained Encoder for Twitter_ at [EMNLP 2022](https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.415) |
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- **Leaderboard:** N/A |
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- **Point of Contact:** Alexandra DeLucia aadelucia (at) jhu.edu |
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### Dataset Summary |
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Tweet IDs for the 2.5 billion multilingual tweets used to train Bernice, a Twitter encoder. |
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Read the paper [here](https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.415). |
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The tweets are from the public 1% Twitter API stream from January 2016 to December 2021. |
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Twitter-provided language metadata is provided with the tweet ID. The data contains 66 unique languages, as identified by [ISO 639 language codes](https://www.wikiwand.com/en/List_of_ISO_639-1_codes), including `und` for undefined languages. |
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Tweets need to be re-gathered via the Twitter API. We suggest [Hydrator](https://github.com/DocNow/hydrator) or [tweepy](https://www.tweepy.org/). |
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### Supported Tasks and Leaderboards |
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N/A |
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### Languages |
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65 languages (ISO 639 codes shown below), plus an `und` (undefined) category. |
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All language identification provided by Twitter API. |
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| | | | | | | | |
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|----|-----|----|----|----|-----|----| |
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| en | ru | ht | zh | bn | ps | lt | |
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| es | bo | ur | ta | sr | ckb | km | |
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| pt | it | sv | ro | bg | si | dv | |
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| ja | th | ca | no | mr | hy | lo | |
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| ar | de | el | uk | ml | or | ug | |
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| in | hi | fi | cy | is | pa | | |
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| ko | pl | cs | ne | te | am | | |
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| tr | nl | iw | hu | gu | sd | | |
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| fr | fa | da | eu | kn | my | | |
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| tl | et | vi | sl | lv | ka | | |
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## Dataset Structure |
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### Data Instances |
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Data is provided in gzip'd files organized by year and month of tweet origin. |
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Tweets are one per line, with fields separated by tabs. |
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### Data Fields |
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* `tweet ID`: ID of tweet |
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* `lang`: ISO 639 code of language, provided by Twitter metadata. Accuracy of label is not known. |
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* `year`: Year tweet was created. Year is also provided in the file names. |
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### Data Splits |
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[More Information Needed] |
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## Dataset Creation |
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### Curation Rationale |
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Data was gathered to support the training of Bernice, a multilingual pre-trained Twitter encoder. |
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### Source Data |
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#### Initial Data Collection and Normalization |
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Data was gathered via the Twitter API public 1% stream from January 2016 through December 2021. |
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Tweets with less than three non-username or URL space-delimited words were removed. |
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All usernames and URLs were replaced with `@USER` and `HTTPURL`, respectively. |
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#### Who are the source language producers? |
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Data was produced by users on Twitter. |
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### Annotations |
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N/A |
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### Personal and Sensitive Information |
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As per Twitter guidelines, only tweet IDs and not full tweets are shared. |
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Tweets will only be accessible if user has not removed their account (or been banned), tweets were deleted or removed, or a user changed their account access to private. |
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## Considerations for Using the Data |
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### Social Impact of Dataset |
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[More Information Needed] |
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### Discussion of Biases |
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[More Information Needed] |
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### Other Known Limitations |
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[More Information Needed] |
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## Additional Information |
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### Dataset Curators |
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Dataset gathered and processed by Mark Dredze, Alexandra DeLucia, Shijie Wu, Aaron Mueller, Carlos Aguirre, and Philip Resnik. |
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### Licensing Information |
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MIT |
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### Citation Information |
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Please cite the Bernice paper if you use this dataset: |
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> Alexandra DeLucia, Shijie Wu, Aaron Mueller, Carlos Aguirre, Philip Resnik, and Mark Dredze. 2022. Bernice: A Multilingual Pre-trained Encoder for Twitter. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 6191–6205, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics. |
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### Contributions |
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Dataset uploaded by [@AADeLucia](https://github.com/AADeLucia). |
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