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

Ko Train

@kaggle.thedevastator_lombard_language_training_dataset.ko_train

1.5 MB
20000 rows
4 columns


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

Ko Validation

@kaggle.thedevastator_lombard_language_training_dataset.ko_validation

786.27 KB
10000 rows
4 columns


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

Ksh Test

@kaggle.thedevastator_lombard_language_training_dataset.ksh_test

12.45 KB
100 rows
4 columns


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

Ksh Train

@kaggle.thedevastator_lombard_language_training_dataset.ksh_train

12.46 KB
100 rows
4 columns


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

Ksh Validation

@kaggle.thedevastator_lombard_language_training_dataset.ksh_validation

12.84 KB
100 rows
4 columns


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

Ku Test

@kaggle.thedevastator_lombard_language_training_dataset.ku_test

10.78 KB
100 rows
4 columns


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

Ku Train

@kaggle.thedevastator_lombard_language_training_dataset.ku_train

11.38 KB
100 rows
4 columns


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

Ku Validation

@kaggle.thedevastator_lombard_language_training_dataset.ku_validation

11.64 KB
100 rows
4 columns


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

Ky Test

@kaggle.thedevastator_lombard_language_training_dataset.ky_test

15.22 KB
100 rows
4 columns


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

Ky Train

@kaggle.thedevastator_lombard_language_training_dataset.ky_train

14.92 KB
100 rows
4 columns


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

Ky Validation

@kaggle.thedevastator_lombard_language_training_dataset.ky_validation

15.48 KB
100 rows
4 columns


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

La Test

@kaggle.thedevastator_lombard_language_training_dataset.la_test

50.54 KB
1000 rows
4 columns


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

La Train

@kaggle.thedevastator_lombard_language_training_dataset.la_train

201.31 KB
5000 rows
4 columns


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

La Validation

@kaggle.thedevastator_lombard_language_training_dataset.la_validation

52.39 KB
1000 rows
4 columns


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

Lb Test

@kaggle.thedevastator_lombard_language_training_dataset.lb_test

72.35 KB
1000 rows
4 columns


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

Lb Train

@kaggle.thedevastator_lombard_language_training_dataset.lb_train

314.88 KB
5000 rows
4 columns


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

Lb Validation

@kaggle.thedevastator_lombard_language_training_dataset.lb_validation

73.31 KB
1000 rows
4 columns


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

Lij Test

@kaggle.thedevastator_lombard_language_training_dataset.lij_test

13.57 KB
100 rows
4 columns


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

Lij Train

@kaggle.thedevastator_lombard_language_training_dataset.lij_train

14.82 KB
100 rows
4 columns


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

Lij Validation

@kaggle.thedevastator_lombard_language_training_dataset.lij_validation

13.08 KB
100 rows
4 columns


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

Li Test

@kaggle.thedevastator_lombard_language_training_dataset.li_test

10.22 KB
100 rows
4 columns


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

Li Train

@kaggle.thedevastator_lombard_language_training_dataset.li_train

10.71 KB
100 rows
4 columns


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

Li Validation

@kaggle.thedevastator_lombard_language_training_dataset.li_validation

11.07 KB
100 rows
4 columns


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

Lmo Test

@kaggle.thedevastator_lombard_language_training_dataset.lmo_test

12.99 KB
100 rows
4 columns


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

Lmo Train

@kaggle.thedevastator_lombard_language_training_dataset.lmo_train

11.07 KB
100 rows
4 columns


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