Hugging Face's logo

Datasets:
mansoorhamidzadeh
/
Persian-NER-Dataset-500k

Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Languages:
Persian
Size:
100K - 1M
Tags:
persian
farsi
ner
name entity recognition
persian ner
Libraries:
Datasets
pandas
License:
Dataset card Viewer Files Community
1
Persian-NER-Dataset-500k / README.md
mansoorhamidzadeh's picture
mansoorhamidzadeh
Update README.md
b6d75d0 verified
preview code
|
raw
history blame contribute delete
6.48 kB
metadata
dataset_info:
  features:
    - name: tokens
      sequence: string
    - name: ner_tags
      sequence: string
    - name: ner_tags_index
      sequence: int64
  splits:
    - name: train
      num_bytes: 177058008
      num_examples: 446243
    - name: validation
      num_bytes: 22025192
      num_examples: 55780
    - name: test
      num_bytes: 22202539
      num_examples: 55781
  download_size: 46003884
  dataset_size: 221285739
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
license: apache-2.0
task_categories:
  - token-classification
language:
  - fa
tags:
  - persian
  - farsi
  - ner
  - name entity recognition
  - persian ner
size_categories:
  - 100K<n<1M

Persian-NER-Dataset-500k

This repository contains a comprehensive Persian Named Entity Recognition (NER) dataset with approximately 500,000 tokens. This dataset is a collection of all available Persian NER datasets, carefully cleaned and consolidated to ensure the highest quality for training, validating, and testing NER models in the Persian language. The dataset is divided into three subsets: training, validation, and test.

Updating...

Dataset Overview

The dataset is structured as follows:

  • Training Set:

    • Number of rows: 446,243
    • Features: ner_tags, tokens, ner_tags_index

  • Validation Set:

    • Number of rows: 55,780
    • Features: ner_tags, tokens, ner_tags_index

  • Test Set:

    • Number of rows: 55,781
    • Features: ner_tags, tokens, ner_tags_index

Features

  • ner_tags: The Named Entity Recognition tags associated with each token.
  • tokens: The tokens (words) that have been tagged.
  • ner_tags_index: The index values corresponding to the NER tags, which map to specific named entities.

NER Tags

The dataset includes the following NER tags:

  • EVENT: Events, including historically significant events or recurring cultural phenomena, etc.
  • NORP: Nationalities, religious or political groups, etc.
  • TIME: Times less specific than dates, including years, seasons, and general times of day, etc.
  • MONEY: Monetary values, including unit, etc.
  • DATE: Absolute or relative dates or periods, etc.
  • MED: Medicines, including drugs and their categories, etc.
  • WORK_OF_ART: Titles of books, songs, and other creative works, etc.
  • PERCENT: Percentage expressions, etc.
  • FAC: Buildings, airports, highways, bridges, and other facilities, etc.
  • LANGUAGE: Any named language, etc.
  • ORG: Organizations, including companies, institutions, government bodies, etc.
  • PERSON: People, including fictional, etc.
  • GPE: Countries, cities, states, etc.
  • LAW: Named documents made into laws, etc.
  • QUANTITY: Measurements that are not monetary or percentages, etc.
  • MISC: Miscellaneous entities, catch-all for entities that do not fit in the above categories, etc.
  • DISEASE: Names of diseases and medical conditions, etc.
  • PRODUCT: Objects, vehicles, foods, etc., that are products or commodities, etc.
  • ORDINAL: "First", "second", etc.
  • CARDINAL: Cardinal numbers, etc.

Tag Counts

Below is a table summarizing the counts of each NER tag across the training, validation, and test datasets:

NER Tag Train Validation Test
B-ORG 74,751 9,177 9,397
I-ORG 93,323 11,065 11,581
B-PRODUCT 35,624 4,515 4,441
I-PRODUCT 31,539 3,995 3,975
B-PERSON 160,596 19,785 19,932
I-PERSON 161,614 19,912 20,195
B-NORP 44,956 5,445 5,751
I-NORP 58,288 6,986 7,502
B-GPE 133,887 16,638 16,517
I-GPE 73,628 8,993 8,987
B-WORK_OF_ART 65,521 8,268 8,134
I-WORK_OF_ART 84,308 10,770 10,461
B-CARDINAL 6,560 880 875
I-CARDINAL 2,059 243 258
I-GPE 73,628 8,993 8,987
B-MED 9,347 1,173 1,177
B-EVENT 1,885 233 231
I-EVENT 4,422 579 532
B-FAC 10,167 1,305 1,261
I-FAC 12,465 1,599 1,501
B-DATE 11,712 1,407 1,451
I-DATE 12,323 1,537 1,544
B-QUANTITY 632 79 78
I-QUANTITY 1,162 155 146
B-MONEY 2,514 325 331
I-MONEY 5,383 738 726
B-TIME 1,468 179 200
I-TIME 1,844 234 266
B-DISEASE 8,524 1,072 1,021
B-ORDINAL 1,486 181 182
I-DISEASE 8,783 1,135 1,086
B-LAW 241 46 50
I-MED 4,457 605 600
B-MISC 3,477 474 435
I-LAW 498 103 131
B-PERCENT 1,895 249 190
I-PERCENT 2,340 313 243
I-MISC 1,819 238 210
B-LANGUAGE 280 38 25
I-ORDINAL 19 6 3
I-LANGUAGE 19 4 6

Usage

This dataset can be used to train, validate, and test NER models in the Persian language. The structured format of the dataset allows easy integration into various deep learning frameworks like TensorFlow, PyTorch, or Hugging Face's transformers.

Example Code

Here's an example of how you might load and use this dataset in Python using the datasets library:

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("mansoorhamidzadeh/Persian-NER-Dataset-500k")

# Access the train, validation, and test sets
train_dataset = dataset['train']
validation_dataset = dataset['validation']
test_dataset = dataset['test']

# Example: Accessing the first example from the train set
print(train_dataset[0])

@dataset{hamidzadeh2024persian_ner_500k,
  author       = {Mansoor Hamidzadeh},
  title        = {Persian-NER-Dataset-500k},
  year         = 2024,
  url          = {https://huggingface.co./datasets/mansoorhamidzadeh/Persian-NER-Dataset-500k}
}