---
language:
- fa
task_categories:
- token-classification
pretty_name: ParsTwiNER
dataset_info:
  features:
  - name: tokens
    sequence: string
  - name: ner_tags
    sequence:
      class_label:
        names:
          '0': O
          '1': B-POG
          '2': I-POG
          '3': B-PER
          '4': I-PER
          '5': B-ORG
          '6': I-ORG
          '7': B-NAT
          '8': I-NAT
          '9': B-LOC
          '10': I-LOC
          '11': B-EVE
          '12': I-EVE
  splits:
  - name: train
    num_bytes: 4434479
    num_examples: 6865
  - name: test
    num_bytes: 198933
    num_examples: 304
  download_size: 1041183
  dataset_size: 4633412
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---
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.