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

Be Validation

@kaggle.thedevastator_lombard_language_training_dataset.be_validation

79.74 KB
1000 rows
4 columns


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

Be X Old Test

@kaggle.thedevastator_lombard_language_training_dataset.be_x_old_test

87.68 KB
1000 rows
4 columns


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

Be X Old Train

@kaggle.thedevastator_lombard_language_training_dataset.be_x_old_train

390.08 KB
5000 rows
4 columns


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

Be X Old Validation

@kaggle.thedevastator_lombard_language_training_dataset.be_x_old_validation

89.72 KB
1000 rows
4 columns


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

Bg Test

@kaggle.thedevastator_lombard_language_training_dataset.bg_test

704.48 KB
10000 rows
4 columns


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

Bg Train

@kaggle.thedevastator_lombard_language_training_dataset.bg_train

1.34 MB
20000 rows
4 columns


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

Bg Validation

@kaggle.thedevastator_lombard_language_training_dataset.bg_validation

711.22 KB
10000 rows
4 columns


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

Bh Test

@kaggle.thedevastator_lombard_language_training_dataset.bh_test

15 KB
100 rows
4 columns


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

Bh Train

@kaggle.thedevastator_lombard_language_training_dataset.bh_train

18.54 KB
100 rows
4 columns


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

Bh Validation

@kaggle.thedevastator_lombard_language_training_dataset.bh_validation

14.94 KB
100 rows
4 columns


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

Bn Test

@kaggle.thedevastator_lombard_language_training_dataset.bn_test

60.59 KB
1000 rows
4 columns


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

Bn Train

@kaggle.thedevastator_lombard_language_training_dataset.bn_train

322.93 KB
10000 rows
4 columns


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

Bn Validation

@kaggle.thedevastator_lombard_language_training_dataset.bn_validation

60.48 KB
1000 rows
4 columns


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

Bo Test

@kaggle.thedevastator_lombard_language_training_dataset.bo_test

9.02 KB
100 rows
4 columns


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

Bo Train

@kaggle.thedevastator_lombard_language_training_dataset.bo_train

8.04 KB
100 rows
4 columns


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

Bo Validation

@kaggle.thedevastator_lombard_language_training_dataset.bo_validation

12 KB
100 rows
4 columns


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

Br Test

@kaggle.thedevastator_lombard_language_training_dataset.br_test

66.6 KB
1000 rows
4 columns


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

Br Train

@kaggle.thedevastator_lombard_language_training_dataset.br_train

67.27 KB
1000 rows
4 columns


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

Br Validation

@kaggle.thedevastator_lombard_language_training_dataset.br_validation

61.89 KB
1000 rows
4 columns


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

Bs Test

@kaggle.thedevastator_lombard_language_training_dataset.bs_test

68.75 KB
1000 rows
4 columns


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

Bs Train

@kaggle.thedevastator_lombard_language_training_dataset.bs_train

831.2 KB
15000 rows
4 columns


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

Bs Validation

@kaggle.thedevastator_lombard_language_training_dataset.bs_validation

70.95 KB
1000 rows
4 columns


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

Ca Test

@kaggle.thedevastator_lombard_language_training_dataset.ca_test

520.52 KB
10000 rows
4 columns


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

Ca Train

@kaggle.thedevastator_lombard_language_training_dataset.ca_train

979.64 KB
20000 rows
4 columns


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

Ca Validation

@kaggle.thedevastator_lombard_language_training_dataset.ca_validation

515.52 KB
10000 rows
4 columns


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