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

It Test

@kaggle.thedevastator_lombard_language_training_dataset.it_test

649.17 KB
10000 rows
4 columns


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

It Train

@kaggle.thedevastator_lombard_language_training_dataset.it_train

1.23 MB
20000 rows
4 columns


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

It Validation

@kaggle.thedevastator_lombard_language_training_dataset.it_validation

640.57 KB
10000 rows
4 columns


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

Ja Test

@kaggle.thedevastator_lombard_language_training_dataset.ja_test

1.15 MB
10000 rows
4 columns


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

Ja Train

@kaggle.thedevastator_lombard_language_training_dataset.ja_train

2.21 MB
20000 rows
4 columns


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

Ja Validation

@kaggle.thedevastator_lombard_language_training_dataset.ja_validation

1.14 MB
10000 rows
4 columns


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

Jbo Test

@kaggle.thedevastator_lombard_language_training_dataset.jbo_test

12.71 KB
100 rows
4 columns


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

Jbo Train

@kaggle.thedevastator_lombard_language_training_dataset.jbo_train

9.36 KB
100 rows
4 columns


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

Jbo Validation

@kaggle.thedevastator_lombard_language_training_dataset.jbo_validation

10.54 KB
100 rows
4 columns


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

Jv Test

@kaggle.thedevastator_lombard_language_training_dataset.jv_test

10.52 KB
100 rows
4 columns


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

Jv Train

@kaggle.thedevastator_lombard_language_training_dataset.jv_train

11.14 KB
100 rows
4 columns


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

Jv Validation

@kaggle.thedevastator_lombard_language_training_dataset.jv_validation

9.73 KB
100 rows
4 columns


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

Ka Test

@kaggle.thedevastator_lombard_language_training_dataset.ka_test

864.64 KB
10000 rows
4 columns


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

Ka Train

@kaggle.thedevastator_lombard_language_training_dataset.ka_train

856.28 KB
10000 rows
4 columns


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

Ka Validation

@kaggle.thedevastator_lombard_language_training_dataset.ka_validation

856.74 KB
10000 rows
4 columns


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

Kk Test

@kaggle.thedevastator_lombard_language_training_dataset.kk_test

73.79 KB
1000 rows
4 columns


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

Kk Train

@kaggle.thedevastator_lombard_language_training_dataset.kk_train

74.9 KB
1000 rows
4 columns


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

Kk Validation

@kaggle.thedevastator_lombard_language_training_dataset.kk_validation

74.44 KB
1000 rows
4 columns


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

Km Test

@kaggle.thedevastator_lombard_language_training_dataset.km_test

18.13 KB
100 rows
4 columns


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

Km Train

@kaggle.thedevastator_lombard_language_training_dataset.km_train

17.39 KB
100 rows
4 columns


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

Km Validation

@kaggle.thedevastator_lombard_language_training_dataset.km_validation

17.35 KB
100 rows
4 columns


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

Kn Test

@kaggle.thedevastator_lombard_language_training_dataset.kn_test

14.89 KB
100 rows
4 columns


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

Kn Train

@kaggle.thedevastator_lombard_language_training_dataset.kn_train

15.58 KB
100 rows
4 columns


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

Kn Validation

@kaggle.thedevastator_lombard_language_training_dataset.kn_validation

16.55 KB
100 rows
4 columns


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

Ko Test

@kaggle.thedevastator_lombard_language_training_dataset.ko_test

785.46 KB
10000 rows
4 columns


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