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

Cs Train

@kaggle.thedevastator_lombard_language_training_dataset.cs_train

1.29 MB
20000 rows
4 columns


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

Cs Validation

@kaggle.thedevastator_lombard_language_training_dataset.cs_validation

678.25 KB
10000 rows
4 columns


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

Cv Test

@kaggle.thedevastator_lombard_language_training_dataset.cv_test

11.92 KB
100 rows
4 columns


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

Cv Train

@kaggle.thedevastator_lombard_language_training_dataset.cv_train

12.36 KB
100 rows
4 columns


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

Cv Validation

@kaggle.thedevastator_lombard_language_training_dataset.cv_validation

10.98 KB
100 rows
4 columns


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

Cy Test

@kaggle.thedevastator_lombard_language_training_dataset.cy_test

55.48 KB
1000 rows
4 columns


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

Cy Train

@kaggle.thedevastator_lombard_language_training_dataset.cy_train

425.94 KB
10000 rows
4 columns


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

Cy Validation

@kaggle.thedevastator_lombard_language_training_dataset.cy_validation

52.8 KB
1000 rows
4 columns


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

Da Test

@kaggle.thedevastator_lombard_language_training_dataset.da_test

666.39 KB
10000 rows
4 columns


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

Da Train

@kaggle.thedevastator_lombard_language_training_dataset.da_train

1.26 MB
20000 rows
4 columns


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

Da Validation

@kaggle.thedevastator_lombard_language_training_dataset.da_validation

666.87 KB
10000 rows
4 columns


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

De Test

@kaggle.thedevastator_lombard_language_training_dataset.de_test

789.97 KB
10000 rows
4 columns


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

De Train

@kaggle.thedevastator_lombard_language_training_dataset.de_train

1.52 MB
20000 rows
4 columns


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

De Validation

@kaggle.thedevastator_lombard_language_training_dataset.de_validation

787.24 KB
10000 rows
4 columns


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

Diq Test

@kaggle.thedevastator_lombard_language_training_dataset.diq_test

10.65 KB
100 rows
4 columns


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

Diq Train

@kaggle.thedevastator_lombard_language_training_dataset.diq_train

11.02 KB
100 rows
4 columns


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

Diq Validation

@kaggle.thedevastator_lombard_language_training_dataset.diq_validation

11.16 KB
100 rows
4 columns


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

Dv Test

@kaggle.thedevastator_lombard_language_training_dataset.dv_test

12.93 KB
100 rows
4 columns


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

Dv Train

@kaggle.thedevastator_lombard_language_training_dataset.dv_train

13.9 KB
100 rows
4 columns


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

Dv Validation

@kaggle.thedevastator_lombard_language_training_dataset.dv_validation

14.35 KB
100 rows
4 columns


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

El Test

@kaggle.thedevastator_lombard_language_training_dataset.el_test

762.77 KB
10000 rows
4 columns


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

El Train

@kaggle.thedevastator_lombard_language_training_dataset.el_train

1.46 MB
20000 rows
4 columns


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

El Validation

@kaggle.thedevastator_lombard_language_training_dataset.el_validation

765.31 KB
10000 rows
4 columns


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

Eml Test

@kaggle.thedevastator_lombard_language_training_dataset.eml_test

14.83 KB
100 rows
4 columns


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

Eml Train

@kaggle.thedevastator_lombard_language_training_dataset.eml_train

13.42 KB
100 rows
4 columns


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