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

So Validation

@kaggle.thedevastator_lombard_language_training_dataset.so_validation

11.95 kB
100 rows
4 columns

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

Sq Test

@kaggle.thedevastator_lombard_language_training_dataset.sq_test

53.66 kB
1,000 rows
4 columns

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

Sq Train

@kaggle.thedevastator_lombard_language_training_dataset.sq_train

196.11 kB
5,000 rows
4 columns

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

Sq Validation

@kaggle.thedevastator_lombard_language_training_dataset.sq_validation

52.34 kB
1,000 rows
4 columns

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

Sr Test

@kaggle.thedevastator_lombard_language_training_dataset.sr_test

701.56 kB
10,000 rows
4 columns

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

Sr Train

@kaggle.thedevastator_lombard_language_training_dataset.sr_train

1.35 MB
20,000 rows
4 columns

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

Sr Validation

@kaggle.thedevastator_lombard_language_training_dataset.sr_validation

694.14 kB
10,000 rows
4 columns

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

Su Test

@kaggle.thedevastator_lombard_language_training_dataset.su_test

11.63 kB
100 rows
4 columns

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

Su Train

@kaggle.thedevastator_lombard_language_training_dataset.su_train

11.18 kB
100 rows
4 columns

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

Su Validation

@kaggle.thedevastator_lombard_language_training_dataset.su_validation

12.2 kB
100 rows
4 columns

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

Sv Test

@kaggle.thedevastator_lombard_language_training_dataset.sv_test

566 kB
10,000 rows
4 columns

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

Sv Train

@kaggle.thedevastator_lombard_language_training_dataset.sv_train

1.09 MB
20,000 rows
4 columns

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

Sv Validation

@kaggle.thedevastator_lombard_language_training_dataset.sv_validation

567.16 kB
10,000 rows
4 columns

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

Sw Test

@kaggle.thedevastator_lombard_language_training_dataset.sw_test

45.99 kB
1,000 rows
4 columns

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

Sw Train

@kaggle.thedevastator_lombard_language_training_dataset.sw_train

46.24 kB
1,000 rows
4 columns

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

Sw Validation

@kaggle.thedevastator_lombard_language_training_dataset.sw_validation

45.86 kB
1,000 rows
4 columns

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

Szl Test

@kaggle.thedevastator_lombard_language_training_dataset.szl_test

10.86 kB
100 rows
4 columns

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

Szl Train

@kaggle.thedevastator_lombard_language_training_dataset.szl_train

10.43 kB
100 rows
4 columns

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

Szl Validation

@kaggle.thedevastator_lombard_language_training_dataset.szl_validation

10.51 kB
100 rows
4 columns

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

Ta Test

@kaggle.thedevastator_lombard_language_training_dataset.ta_test

105.57 kB
1,000 rows
4 columns

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

Ta Train

@kaggle.thedevastator_lombard_language_training_dataset.ta_train

1.31 MB
15,000 rows
4 columns

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

Ta Validation

@kaggle.thedevastator_lombard_language_training_dataset.ta_validation

103.95 kB
1,000 rows
4 columns

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

Te Test

@kaggle.thedevastator_lombard_language_training_dataset.te_test

98.87 kB
1,000 rows
4 columns

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

Te Train

@kaggle.thedevastator_lombard_language_training_dataset.te_train

99.25 kB
1,000 rows
4 columns

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

Te Validation

@kaggle.thedevastator_lombard_language_training_dataset.te_validation

97.68 kB
1,000 rows
4 columns

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