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

Tg Test

@kaggle.thedevastator_lombard_language_training_dataset.tg_test

12.55 KB
100 rows
4 columns


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

Tg Train

@kaggle.thedevastator_lombard_language_training_dataset.tg_train

13.26 KB
100 rows
4 columns


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

Tg Validation

@kaggle.thedevastator_lombard_language_training_dataset.tg_validation

12.06 KB
100 rows
4 columns


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

Th Test

@kaggle.thedevastator_lombard_language_training_dataset.th_test

1.71 MB
10000 rows
4 columns


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

Th Train

@kaggle.thedevastator_lombard_language_training_dataset.th_train

3.41 MB
20000 rows
4 columns


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

Th Validation

@kaggle.thedevastator_lombard_language_training_dataset.th_validation

1.67 MB
10000 rows
4 columns


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

Tk Test

@kaggle.thedevastator_lombard_language_training_dataset.tk_test

12.05 KB
100 rows
4 columns


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

Tk Train

@kaggle.thedevastator_lombard_language_training_dataset.tk_train

11.12 KB
100 rows
4 columns


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

Tk Validation

@kaggle.thedevastator_lombard_language_training_dataset.tk_validation

12.25 KB
100 rows
4 columns


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

Tl Test

@kaggle.thedevastator_lombard_language_training_dataset.tl_test

45.33 KB
1000 rows
4 columns


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

Tl Train

@kaggle.thedevastator_lombard_language_training_dataset.tl_train

259 KB
10000 rows
4 columns


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

Tl Validation

@kaggle.thedevastator_lombard_language_training_dataset.tl_validation

44.34 KB
1000 rows
4 columns


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

Tr Test

@kaggle.thedevastator_lombard_language_training_dataset.tr_test

668.84 KB
10000 rows
4 columns


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

Tr Train

@kaggle.thedevastator_lombard_language_training_dataset.tr_train

1.24 MB
20000 rows
4 columns


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

Tr Validation

@kaggle.thedevastator_lombard_language_training_dataset.tr_validation

666.93 KB
10000 rows
4 columns


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

Tt Test

@kaggle.thedevastator_lombard_language_training_dataset.tt_test

59.76 KB
1000 rows
4 columns


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

Tt Train

@kaggle.thedevastator_lombard_language_training_dataset.tt_train

60.98 KB
1000 rows
4 columns


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

Tt Validation

@kaggle.thedevastator_lombard_language_training_dataset.tt_validation

62.33 KB
1000 rows
4 columns


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

Ug Test

@kaggle.thedevastator_lombard_language_training_dataset.ug_test

16.7 KB
100 rows
4 columns


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

Ug Train

@kaggle.thedevastator_lombard_language_training_dataset.ug_train

13.61 KB
100 rows
4 columns


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

Ug Validation

@kaggle.thedevastator_lombard_language_training_dataset.ug_validation

17.77 KB
100 rows
4 columns


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

Uk Test

@kaggle.thedevastator_lombard_language_training_dataset.uk_test

729.32 KB
10000 rows
4 columns


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

Uk Train

@kaggle.thedevastator_lombard_language_training_dataset.uk_train

1.41 MB
20000 rows
4 columns


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

Uk Validation

@kaggle.thedevastator_lombard_language_training_dataset.uk_validation

729.61 KB
10000 rows
4 columns


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

Ur Test

@kaggle.thedevastator_lombard_language_training_dataset.ur_test

56.21 KB
1000 rows
4 columns


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