Dataset Open Access

Temporal Model XLM-RoBERTa-Large_Tweets

Kirsanov Simon

Temporal Model "XLM-RoBERTa-Large" finetuned on the "Tweets" dataset to solve the tasks of extraction and classification of temporal entities. Model produced in the master's thesis "Extraction and Classification of Time in Unstructured Data [Kirsanov, 2023]".

 

Files (1.7 GB)
Name Size
xlm-roberta-large_tweets_multi.zip
md5:02d235c83594a247f8119f97b9f35b47 		1.7 GB Download
Publication date:
November 1, 2023
DOI:
10.25592/uhhfdm.13587

DOI Badge

Markdown 

[![DOI](https://www.fdr.uni-hamburg.de/badge/DOI/10.25592/uhhfdm.13587.svg)](https://doi.org/10.25592/uhhfdm.13587)

reStructedText 

.. image:: https://www.fdr.uni-hamburg.de/badge/DOI/10.25592/uhhfdm.13587.svg
   :target: https://doi.org/10.25592/uhhfdm.13587

HTML 

<a href="https://doi.org/10.25592/uhhfdm.13587"><img src="https://www.fdr.uni-hamburg.de/badge/DOI/10.25592/uhhfdm.13587.svg" alt="DOI"></a>

Image URL 

https://www.fdr.uni-hamburg.de/badge/DOI/10.25592/uhhfdm.13587.svg

Target URL 

https://doi.org/10.25592/uhhfdm.13587

Keyword(s):
xlm-roberta temporal tagging temporal extraction information extraction natural language processing nlp named entity recognition ner timex3 tweets
Communities:
License (for files):
Creative Commons Attribution 4.0 International

Versions

Version 1 10.25592/uhhfdm.13587 Nov 1, 2023
Cite all versions? You can cite all versions by using the DOI 10.25592/uhhfdm.13586. This DOI represents all versions, and will always resolve to the latest one.

Cite record as

Export