Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Languages:
Persian
Size:
10K - 100K
ArXiv:
License:
| language: | |
| - fa | |
| license: mit | |
| size_categories: | |
| - 10K<n<100K | |
| task_categories: | |
| - token-classification | |
| pretty_name: PEYMA-ARMAN-Mixed | |
| dataset_info: | |
| features: | |
| - name: tokens | |
| sequence: string | |
| - name: ner_tags | |
| sequence: | |
| class_label: | |
| names: | |
| '0': B_LOC | |
| '1': I_DAT | |
| '2': B_PCT | |
| '3': I_LOC | |
| '4': I_PER | |
| '5': I_MON | |
| '6': B_ORG | |
| '7': B_PRO | |
| '8': B_PER | |
| '9': O | |
| '10': I_PCT | |
| '11': I_ORG | |
| '12': B_FAC | |
| '13': B_DAT | |
| '14': B_TIM | |
| '15': I_TIM | |
| '16': I_EVE | |
| '17': B_MON | |
| '18': I_PRO | |
| '19': B_EVE | |
| '20': I_FAC | |
| - name: ner_tags_names | |
| sequence: string | |
| splits: | |
| - name: train | |
| num_bytes: 21618080 | |
| num_examples: 26384 | |
| - name: validation | |
| num_bytes: 2782070 | |
| num_examples: 3296 | |
| - name: test | |
| num_bytes: 2706143 | |
| num_examples: 3296 | |
| download_size: 4168673 | |
| dataset_size: 27106293 | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-* | |
| - split: validation | |
| path: data/validation-* | |
| - split: test | |
| path: data/test-* | |
| # Mixed Persian NER Dataset (PEYMA-ARMAN) | |
| This dataset is a combination of [PEYMA](https://arxiv.org/abs/1801.09936) and [ARMAN](https://github.com/HaniehP/PersianNER) Persian NER datasets. It contains the following named entity tags: | |
| - Product (PRO) | |
| - Event (EVE) | |
| - Facility (FAC) | |
| - Location (LOC) | |
| - Person (PER) | |
| - Money (MON) | |
| - Percent (PCT) | |
| - Date (DAT) | |
| - Organization (ORG) | |
| - Time (TIM) | |
| ## Dataset Information | |
| The dataset is divided into three splits: train, test, and validation. Below is a summary of the dataset statistics: | |
| | Split | B_DAT | B_EVE | B_FAC | B_LOC | B_MON | B_ORG | B_PCT | B_PER | B_PRO | B_TIM | I_DAT | I_EVE | I_FAC | I_LOC | I_MON | I_ORG | I_PCT | I_PER | I_PRO | I_TIM | O | num_rows | | |
| |------------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|----------| | |
| | Train | 1512 | 1379 | 1334 | 13040 | 446 | 15762 | 266 | 11371 | 1719 | 224 | 1939 | 4600 | 2222 | 4254 | 1314 | 21347 | 308 | 7160 | 1736 | 375 | 747216 | 26417 | | |
| | Test | 185 | 218 | 124 | 1868 | 53 | 2017 | 27 | 1566 | 281 | 27 | 245 | 697 | 237 | 511 | 142 | 2843 | 31 | 1075 | 345 | 37 | 92214 | 3303 | | |
| | Validation | 161 | 143 | 192 | 1539 | 28 | 2180 | 33 | 1335 | 172 | 30 | 217 | 520 | 349 | 494 | 54 | 2923 | 34 | 813 | 136 | 39 | 96857 | 3302 | | |
| ### First schema | |
| ```python | |
| DatasetDict({ | |
| train: Dataset({ | |
| features: ['tokens', 'ner_tags', 'ner_tags_names'], | |
| num_rows: 26417 | |
| }) | |
| test: Dataset({ | |
| features: ['tokens', 'ner_tags', 'ner_tags_names'], | |
| num_rows: 3303 | |
| }) | |
| validation: Dataset({ | |
| features: ['tokens', 'ner_tags', 'ner_tags_names'], | |
| num_rows: 3302 | |
| }) | |
| }) | |
| ``` | |
| ### How to load datset | |
| ```python | |
| from datasets import load_dataset | |
| data = load_dataset("AliFartout/PEYMA-ARMAN-Mixed") | |
| ``` | |
| Feel free to adjust the formatting according to your needs. |