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

Mzn Train

@kaggle.thedevastator_lombard_language_training_dataset.mzn_train

11.05 kB
100 rows
4 columns

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

Mzn Validation

@kaggle.thedevastator_lombard_language_training_dataset.mzn_validation

11.69 kB
100 rows
4 columns

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

Nap Test

@kaggle.thedevastator_lombard_language_training_dataset.nap_test

12.96 kB
100 rows
4 columns

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

Nap Train

@kaggle.thedevastator_lombard_language_training_dataset.nap_train

13.52 kB
100 rows
4 columns

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

Nap Validation

@kaggle.thedevastator_lombard_language_training_dataset.nap_validation

12.31 kB
100 rows
4 columns

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

Nds Test

@kaggle.thedevastator_lombard_language_training_dataset.nds_test

12.68 kB
100 rows
4 columns

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

Nds Train

@kaggle.thedevastator_lombard_language_training_dataset.nds_train

12.37 kB
100 rows
4 columns

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

Nds Validation

@kaggle.thedevastator_lombard_language_training_dataset.nds_validation

13.92 kB
100 rows
4 columns

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

Ne Test

@kaggle.thedevastator_lombard_language_training_dataset.ne_test

14.98 kB
100 rows
4 columns

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

Ne Train

@kaggle.thedevastator_lombard_language_training_dataset.ne_train

16.06 kB
100 rows
4 columns

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

Ne Validation

@kaggle.thedevastator_lombard_language_training_dataset.ne_validation

15.11 kB
100 rows
4 columns

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

Nl Test

@kaggle.thedevastator_lombard_language_training_dataset.nl_test

673.34 kB
10,000 rows
4 columns

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

Nl Train

@kaggle.thedevastator_lombard_language_training_dataset.nl_train

1.3 MB
20,000 rows
4 columns

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

Nl Validation

@kaggle.thedevastator_lombard_language_training_dataset.nl_validation

660.94 kB
10,000 rows
4 columns

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

Nn Test

@kaggle.thedevastator_lombard_language_training_dataset.nn_test

80.88 kB
1,000 rows
4 columns

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

Nn Train

@kaggle.thedevastator_lombard_language_training_dataset.nn_train

1.36 MB
20,000 rows
4 columns

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

Nn Validation

@kaggle.thedevastator_lombard_language_training_dataset.nn_validation

83.74 kB
1,000 rows
4 columns

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

No Test

@kaggle.thedevastator_lombard_language_training_dataset.no_test

725.16 kB
10,000 rows
4 columns

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

No Train

@kaggle.thedevastator_lombard_language_training_dataset.no_train

1.42 MB
20,000 rows
4 columns

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

No Validation

@kaggle.thedevastator_lombard_language_training_dataset.no_validation

733.36 kB
10,000 rows
4 columns

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

Nov Test

@kaggle.thedevastator_lombard_language_training_dataset.nov_test

7.91 kB
100 rows
4 columns

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

Nov Train

@kaggle.thedevastator_lombard_language_training_dataset.nov_train

8.61 kB
100 rows
4 columns

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

Nov Validation

@kaggle.thedevastator_lombard_language_training_dataset.nov_validation

10.3 kB
100 rows
4 columns

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

Oc Test

@kaggle.thedevastator_lombard_language_training_dataset.oc_test

10.39 kB
100 rows
4 columns

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

Oc Train

@kaggle.thedevastator_lombard_language_training_dataset.oc_train

10.66 kB
100 rows
4 columns

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