ParsTwiNER dataset created by Aghajani et al. [Paper](https://paperswithcode.com/paper/parstwiner-a-corpus-for-named-entity)

> As a result of unstructured sentences and some misspellings and errors, finding named entities in a noisy environment such as social media takes much more effort.
> ParsTwiNER contains about 250k tokens, based on standard instructions like MUC-6 or CoNLL 2003, gathered from Persian Twitter. Using Cohen’s Kappa coefficient, the consistency of annotators is 0.95, a high score.
> In this study, we demonstrate that some state-of-the-art models degrade on these corpora, and trained a new model using parallel transfer learning based on the BERT architecture.
> Experimental results show that the model works well in informal Persian as well as in formal Persian.
task_categories:token-classification language:fa region:us
{"language": ["fa"], "task_categories": ["token-classification"], "pretty_name": "ParsTwiNER"}
TAGS
#task_categories-token-classification #language-Persian #region-us 

ParsTwiNER dataset created by Aghajani et al. Paper

> As a result of unstructured sentences and some misspellings and errors, finding named entities in a noisy environment such as social media takes much more effort.
> ParsTwiNER contains about 250k tokens, based on standard instructions like MUC-6 or CoNLL 2003, gathered from Persian Twitter. Using Cohen’s Kappa coefficient, the consistency of annotators is 0.95, a high score.
> In this study, we demonstrate that some state-of-the-art models degrade on these corpora, and trained a new model using parallel transfer learning based on the BERT architecture.
> Experimental results show that the model works well in informal Persian as well as in formal Persian.
TAGS
#task_categories-token-classification #language-Persian #region-us 
