metadata
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
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.