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--- |
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language: |
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- fa |
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task_categories: |
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- token-classification |
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pretty_name: ParsTwiNER |
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dataset_info: |
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features: |
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- name: tokens |
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sequence: string |
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- name: ner_tags |
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sequence: |
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class_label: |
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names: |
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'0': O |
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'1': B-POG |
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'2': I-POG |
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'3': B-PER |
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'4': I-PER |
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'5': B-ORG |
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'6': I-ORG |
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'7': B-NAT |
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'8': I-NAT |
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'9': B-LOC |
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'10': I-LOC |
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'11': B-EVE |
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'12': I-EVE |
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splits: |
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- name: train |
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num_bytes: 4434479 |
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num_examples: 6865 |
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- name: test |
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num_bytes: 198933 |
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num_examples: 304 |
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download_size: 1041183 |
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dataset_size: 4633412 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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--- |
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ParsTwiNER dataset created by Aghajani et al. [Paper](https://paperswithcode.com/paper/parstwiner-a-corpus-for-named-entity) |
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> 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. |
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> 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. |
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> 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. |
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> Experimental results show that the model works well in informal Persian as well as in formal Persian. |
