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

Ur Train

@kaggle.thedevastator_lombard_language_training_dataset.ur_train

737.29 KB
20000 rows
4 columns


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

Ur Validation

@kaggle.thedevastator_lombard_language_training_dataset.ur_validation

55.35 KB
1000 rows
4 columns


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

Uz Test

@kaggle.thedevastator_lombard_language_training_dataset.uz_test

38.44 KB
1000 rows
4 columns


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

Uz Train

@kaggle.thedevastator_lombard_language_training_dataset.uz_train

35.83 KB
1000 rows
4 columns


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

Uz Validation

@kaggle.thedevastator_lombard_language_training_dataset.uz_validation

38.17 KB
1000 rows
4 columns


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

Vec Test

@kaggle.thedevastator_lombard_language_training_dataset.vec_test

11.04 KB
100 rows
4 columns


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

Vec Train

@kaggle.thedevastator_lombard_language_training_dataset.vec_train

10.68 KB
100 rows
4 columns


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

Vec Validation

@kaggle.thedevastator_lombard_language_training_dataset.vec_validation

11.1 KB
100 rows
4 columns


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

Vep Test

@kaggle.thedevastator_lombard_language_training_dataset.vep_test

10.8 KB
100 rows
4 columns


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

Vep Train

@kaggle.thedevastator_lombard_language_training_dataset.vep_train

10.64 KB
100 rows
4 columns


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

Vep Validation

@kaggle.thedevastator_lombard_language_training_dataset.vep_validation

10.85 KB
100 rows
4 columns


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

Vi Test

@kaggle.thedevastator_lombard_language_training_dataset.vi_test

499.1 KB
10000 rows
4 columns


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

Vi Train

@kaggle.thedevastator_lombard_language_training_dataset.vi_train

924.9 KB
20000 rows
4 columns


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

Vi Validation

@kaggle.thedevastator_lombard_language_training_dataset.vi_validation

491.15 KB
10000 rows
4 columns


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

Vls Test

@kaggle.thedevastator_lombard_language_training_dataset.vls_test

12.6 KB
100 rows
4 columns


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

Vls Train

@kaggle.thedevastator_lombard_language_training_dataset.vls_train

12.41 KB
100 rows
4 columns


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

Vls Validation

@kaggle.thedevastator_lombard_language_training_dataset.vls_validation

12.92 KB
100 rows
4 columns


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

Vo Test

@kaggle.thedevastator_lombard_language_training_dataset.vo_test

7.21 KB
100 rows
4 columns


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

Vo Train

@kaggle.thedevastator_lombard_language_training_dataset.vo_train

6.96 KB
100 rows
4 columns


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

Vo Validation

@kaggle.thedevastator_lombard_language_training_dataset.vo_validation

7.12 KB
100 rows
4 columns


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

War Test

@kaggle.thedevastator_lombard_language_training_dataset.war_test

9.81 KB
100 rows
4 columns


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

War Train

@kaggle.thedevastator_lombard_language_training_dataset.war_train

9.67 KB
100 rows
4 columns


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

War Validation

@kaggle.thedevastator_lombard_language_training_dataset.war_validation

9.27 KB
100 rows
4 columns


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

Wa Test

@kaggle.thedevastator_lombard_language_training_dataset.wa_test

10.88 KB
100 rows
4 columns


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

Wa Train

@kaggle.thedevastator_lombard_language_training_dataset.wa_train

11.27 KB
100 rows
4 columns


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