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

Hsb Validation

@kaggle.thedevastator_lombard_language_training_dataset.hsb_validation

11.75 KB
100 rows
4 columns


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

Hu Test

@kaggle.thedevastator_lombard_language_training_dataset.hu_test

745.18 KB
10000 rows
4 columns


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

Hu Train

@kaggle.thedevastator_lombard_language_training_dataset.hu_train

1.43 MB
20000 rows
4 columns


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

Hu Validation

@kaggle.thedevastator_lombard_language_training_dataset.hu_validation

727.88 KB
10000 rows
4 columns


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

Hy Test

@kaggle.thedevastator_lombard_language_training_dataset.hy_test

69.49 KB
1000 rows
4 columns


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

Hy Train

@kaggle.thedevastator_lombard_language_training_dataset.hy_train

722.63 KB
15000 rows
4 columns


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

Hy Validation

@kaggle.thedevastator_lombard_language_training_dataset.hy_validation

70.3 KB
1000 rows
4 columns


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

Ia Test

@kaggle.thedevastator_lombard_language_training_dataset.ia_test

15.36 KB
100 rows
4 columns


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

Ia Train

@kaggle.thedevastator_lombard_language_training_dataset.ia_train

14.53 KB
100 rows
4 columns


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

Ia Validation

@kaggle.thedevastator_lombard_language_training_dataset.ia_validation

14.19 KB
100 rows
4 columns


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

Id Test

@kaggle.thedevastator_lombard_language_training_dataset.id_test

449.4 KB
10000 rows
4 columns


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

Id Train

@kaggle.thedevastator_lombard_language_training_dataset.id_train

853.59 KB
20000 rows
4 columns


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

Id Validation

@kaggle.thedevastator_lombard_language_training_dataset.id_validation

452.97 KB
10000 rows
4 columns


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

Ig Test

@kaggle.thedevastator_lombard_language_training_dataset.ig_test

9.45 KB
100 rows
4 columns


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

Ig Train

@kaggle.thedevastator_lombard_language_training_dataset.ig_train

8.78 KB
100 rows
4 columns


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

Ig Validation

@kaggle.thedevastator_lombard_language_training_dataset.ig_validation

9.69 KB
100 rows
4 columns


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

Ilo Test

@kaggle.thedevastator_lombard_language_training_dataset.ilo_test

8.88 KB
100 rows
4 columns


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

Ilo Train

@kaggle.thedevastator_lombard_language_training_dataset.ilo_train

9.45 KB
100 rows
4 columns


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

Ilo Validation

@kaggle.thedevastator_lombard_language_training_dataset.ilo_validation

8.75 KB
100 rows
4 columns


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

Io Test

@kaggle.thedevastator_lombard_language_training_dataset.io_test

9.63 KB
100 rows
4 columns


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

Io Train

@kaggle.thedevastator_lombard_language_training_dataset.io_train

10.53 KB
100 rows
4 columns


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

Io Validation

@kaggle.thedevastator_lombard_language_training_dataset.io_validation

9.87 KB
100 rows
4 columns


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

Is Test

@kaggle.thedevastator_lombard_language_training_dataset.is_test

70.07 KB
1000 rows
4 columns


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

Is Train

@kaggle.thedevastator_lombard_language_training_dataset.is_train

73.45 KB
1000 rows
4 columns


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

Is Validation

@kaggle.thedevastator_lombard_language_training_dataset.is_validation

72.9 KB
1000 rows
4 columns


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