Name: WikiANN
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

WikiANN

Lombard Language Training Dataset

Lombard Language Training Data

By wikiann (From Huggingface) [source]

About this dataset

Overview

WikiANN (sometimes called PAN-X) is a multilingual named entity recognition dataset consisting of Wikipedia articles annotated with LOC (location), PER (person), and ORG (organisation) tags in the IOB2 format. This version corresponds to the balanced train, dev, and test splits of Rahimi et al. (2019), which supports 176 of the 282 languages from the original WikiANN corpus.

Columns

File: pdc_train.csv

Column name	Description
tokens	This column contains individual words or tokens in the Lombard language. (Text)
ner_tags	This column contains named entity recognition (NER) tags associated with each token. NER tags help identify and classify named entities such as names, locations, organizations, etc. (Text)
langs	This column indicates the language of each token. In this dataset, it specifically represents the Lombard language. (Text)
spans	This column provides information about the position or span of each token within the text. (Text)

File: sr_validation.csv

Column name	Description
tokens	This column contains individual words or tokens in the Lombard language. (Text)
ner_tags	This column contains named entity recognition (NER) tags associated with each token. NER tags help identify and classify named entities such as names, locations, organizations, etc. (Text)
langs	This column indicates the language of each token. In this dataset, it specifically represents the Lombard language. (Text)
spans	This column provides information about the position or span of each token within the text. (Text)

File: uz_train.csv

Column name	Description
tokens	This column contains individual words or tokens in the Lombard language. (Text)
ner_tags	This column contains named entity recognition (NER) tags associated with each token. NER tags help identify and classify named entities such as names, locations, organizations, etc. (Text)
langs	This column indicates the language of each token. In this dataset, it specifically represents the Lombard language. (Text)
spans	This column provides information about the position or span of each token within the text. (Text)

Research Ideas

Named Entity Recognition (NER) Training: The dataset can be used to train models for NER tasks specific to the Lombard language. By utilizing the ner_tags column, developers can create models that identify and classify named entities in Lombard text, such as names of people, places, organizations, and more.

Language Classification: Since the langs column indicates the language of each token in the dataset (which will always be Lombard), this dataset can be used for training language classification models. These models can then be utilized to automatically detect whether a given piece of text is in Lombard or another language.

Span Identification: The spans column provides information about the position or span of each token within the text. This information can be utilized to develop algorithms or applications that require analyzing specific spans within sentences or paragraphs in Lombard text. For example, it could help identify important phrases or extract certain sections of text from a larger document

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit wikiann (From Huggingface).

Tables

Gd Train

@kaggle.thedevastator_lombard_language_training_dataset.gd_train

10.6 KB
100 rows
4 columns


CREATE TABLE gd_train (
  "tokens" VARCHAR,
  "ner_tags" VARCHAR,
  "langs" VARCHAR,
  "spans" VARCHAR
);

Gd Validation

@kaggle.thedevastator_lombard_language_training_dataset.gd_validation

11.46 KB
100 rows
4 columns


CREATE TABLE gd_validation (
  "tokens" VARCHAR,
  "ner_tags" VARCHAR,
  "langs" VARCHAR,
  "spans" VARCHAR
);

Gl Test

@kaggle.thedevastator_lombard_language_training_dataset.gl_test

483.4 KB
10000 rows
4 columns


CREATE TABLE gl_test (
  "tokens" VARCHAR,
  "ner_tags" VARCHAR,
  "langs" VARCHAR,
  "spans" VARCHAR
);

Gl Train

@kaggle.thedevastator_lombard_language_training_dataset.gl_train

696.42 KB
15000 rows
4 columns


CREATE TABLE gl_train (
  "tokens" VARCHAR,
  "ner_tags" VARCHAR,
  "langs" VARCHAR,
  "spans" VARCHAR
);

Gl Validation

@kaggle.thedevastator_lombard_language_training_dataset.gl_validation

483.87 KB
10000 rows
4 columns


CREATE TABLE gl_validation (
  "tokens" VARCHAR,
  "ner_tags" VARCHAR,
  "langs" VARCHAR,
  "spans" VARCHAR
);

Gn Test

@kaggle.thedevastator_lombard_language_training_dataset.gn_test

12.68 KB
100 rows
4 columns


CREATE TABLE gn_test (
  "tokens" VARCHAR,
  "ner_tags" VARCHAR,
  "langs" VARCHAR,
  "spans" VARCHAR
);

Gn Train

@kaggle.thedevastator_lombard_language_training_dataset.gn_train

14.29 KB
100 rows
4 columns


CREATE TABLE gn_train (
  "tokens" VARCHAR,
  "ner_tags" VARCHAR,
  "langs" VARCHAR,
  "spans" VARCHAR
);

Gn Validation

@kaggle.thedevastator_lombard_language_training_dataset.gn_validation

13.97 KB
100 rows
4 columns


CREATE TABLE gn_validation (
  "tokens" VARCHAR,
  "ner_tags" VARCHAR,
  "langs" VARCHAR,
  "spans" VARCHAR
);

Gu Test

@kaggle.thedevastator_lombard_language_training_dataset.gu_test

17.19 KB
100 rows
4 columns


CREATE TABLE gu_test (
  "tokens" VARCHAR,
  "ner_tags" VARCHAR,
  "langs" VARCHAR,
  "spans" VARCHAR
);

Gu Train

@kaggle.thedevastator_lombard_language_training_dataset.gu_train

16.88 KB
100 rows
4 columns


CREATE TABLE gu_train (
  "tokens" VARCHAR,
  "ner_tags" VARCHAR,
  "langs" VARCHAR,
  "spans" VARCHAR
);

Gu Validation

@kaggle.thedevastator_lombard_language_training_dataset.gu_validation

18.26 KB
100 rows
4 columns


CREATE TABLE gu_validation (
  "tokens" VARCHAR,
  "ner_tags" VARCHAR,
  "langs" VARCHAR,
  "spans" VARCHAR
);

Hak Test

@kaggle.thedevastator_lombard_language_training_dataset.hak_test

10.58 KB
100 rows
4 columns


CREATE TABLE hak_test (
  "tokens" VARCHAR,
  "ner_tags" VARCHAR,
  "langs" VARCHAR,
  "spans" VARCHAR
);

Hak Train

@kaggle.thedevastator_lombard_language_training_dataset.hak_train

10.32 KB
100 rows
4 columns


CREATE TABLE hak_train (
  "tokens" VARCHAR,
  "ner_tags" VARCHAR,
  "langs" VARCHAR,
  "spans" VARCHAR
);

Hak Validation

@kaggle.thedevastator_lombard_language_training_dataset.hak_validation

10.54 KB
100 rows
4 columns


CREATE TABLE hak_validation (
  "tokens" VARCHAR,
  "ner_tags" VARCHAR,
  "langs" VARCHAR,
  "spans" VARCHAR
);

He Test

@kaggle.thedevastator_lombard_language_training_dataset.he_test

746.13 KB
10000 rows
4 columns


CREATE TABLE he_test (
  "tokens" VARCHAR,
  "ner_tags" VARCHAR,
  "langs" VARCHAR,
  "spans" VARCHAR
);

He Train

@kaggle.thedevastator_lombard_language_training_dataset.he_train

1.42 MB
20000 rows
4 columns


CREATE TABLE he_train (
  "tokens" VARCHAR,
  "ner_tags" VARCHAR,
  "langs" VARCHAR,
  "spans" VARCHAR
);

He Validation

@kaggle.thedevastator_lombard_language_training_dataset.he_validation

757.36 KB
10000 rows
4 columns


CREATE TABLE he_validation (
  "tokens" VARCHAR,
  "ner_tags" VARCHAR,
  "langs" VARCHAR,
  "spans" VARCHAR
);

Hi Test

@kaggle.thedevastator_lombard_language_training_dataset.hi_test

73.81 KB
1000 rows
4 columns


CREATE TABLE hi_test (
  "tokens" VARCHAR,
  "ner_tags" VARCHAR,
  "langs" VARCHAR,
  "spans" VARCHAR
);

Hi Train

@kaggle.thedevastator_lombard_language_training_dataset.hi_train

290.46 KB
5000 rows
4 columns


CREATE TABLE hi_train (
  "tokens" VARCHAR,
  "ner_tags" VARCHAR,
  "langs" VARCHAR,
  "spans" VARCHAR
);

Hi Validation

@kaggle.thedevastator_lombard_language_training_dataset.hi_validation

72.65 KB
1000 rows
4 columns


CREATE TABLE hi_validation (
  "tokens" VARCHAR,
  "ner_tags" VARCHAR,
  "langs" VARCHAR,
  "spans" VARCHAR
);

Hr Test

@kaggle.thedevastator_lombard_language_training_dataset.hr_test

649.05 KB
10000 rows
4 columns


CREATE TABLE hr_test (
  "tokens" VARCHAR,
  "ner_tags" VARCHAR,
  "langs" VARCHAR,
  "spans" VARCHAR
);

Hr Train

@kaggle.thedevastator_lombard_language_training_dataset.hr_train

1.23 MB
20000 rows
4 columns


CREATE TABLE hr_train (
  "tokens" VARCHAR,
  "ner_tags" VARCHAR,
  "langs" VARCHAR,
  "spans" VARCHAR
);

Hr Validation

@kaggle.thedevastator_lombard_language_training_dataset.hr_validation

649.84 KB
10000 rows
4 columns


CREATE TABLE hr_validation (
  "tokens" VARCHAR,
  "ner_tags" VARCHAR,
  "langs" VARCHAR,
  "spans" VARCHAR
);

Hsb Test

@kaggle.thedevastator_lombard_language_training_dataset.hsb_test

11.98 KB
100 rows
4 columns


CREATE TABLE hsb_test (
  "tokens" VARCHAR,
  "ner_tags" VARCHAR,
  "langs" VARCHAR,
  "spans" VARCHAR
);

Hsb Train

@kaggle.thedevastator_lombard_language_training_dataset.hsb_train

11.72 KB
100 rows
4 columns


CREATE TABLE hsb_train (
  "tokens" VARCHAR,
  "ner_tags" VARCHAR,
  "langs" VARCHAR,
  "spans" VARCHAR
);