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

Scn Train

@kaggle.thedevastator_lombard_language_training_dataset.scn_train

11.36 KB
100 rows
4 columns


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

Scn Validation

@kaggle.thedevastator_lombard_language_training_dataset.scn_validation

10.65 KB
100 rows
4 columns


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

Sco Test

@kaggle.thedevastator_lombard_language_training_dataset.sco_test

11.4 KB
100 rows
4 columns


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

Sco Train

@kaggle.thedevastator_lombard_language_training_dataset.sco_train

10.8 KB
100 rows
4 columns


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

Sco Validation

@kaggle.thedevastator_lombard_language_training_dataset.sco_validation

12.28 KB
100 rows
4 columns


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

Sd Test

@kaggle.thedevastator_lombard_language_training_dataset.sd_test

16.34 KB
100 rows
4 columns


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

Sd Train

@kaggle.thedevastator_lombard_language_training_dataset.sd_train

21.89 KB
100 rows
4 columns


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

Sd Validation

@kaggle.thedevastator_lombard_language_training_dataset.sd_validation

22.28 KB
100 rows
4 columns


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

Sh Test

@kaggle.thedevastator_lombard_language_training_dataset.sh_test

421.57 KB
10000 rows
4 columns


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

Sh Train

@kaggle.thedevastator_lombard_language_training_dataset.sh_train

799.88 KB
20000 rows
4 columns


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

Sh Validation

@kaggle.thedevastator_lombard_language_training_dataset.sh_validation

423.91 KB
10000 rows
4 columns


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

Simple Test

@kaggle.thedevastator_lombard_language_training_dataset.simple_test

63.4 KB
1000 rows
4 columns


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

Simple Train

@kaggle.thedevastator_lombard_language_training_dataset.simple_train

981.23 KB
20000 rows
4 columns


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

Simple Validation

@kaggle.thedevastator_lombard_language_training_dataset.simple_validation

62.56 KB
1000 rows
4 columns


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

Si Test

@kaggle.thedevastator_lombard_language_training_dataset.si_test

12.28 KB
100 rows
4 columns


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

Si Train

@kaggle.thedevastator_lombard_language_training_dataset.si_train

12.9 KB
100 rows
4 columns


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

Si Validation

@kaggle.thedevastator_lombard_language_training_dataset.si_validation

12.74 KB
100 rows
4 columns


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

Sk Test

@kaggle.thedevastator_lombard_language_training_dataset.sk_test

634.99 KB
10000 rows
4 columns


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

Sk Train

@kaggle.thedevastator_lombard_language_training_dataset.sk_train

1.19 MB
20000 rows
4 columns


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

Sk Validation

@kaggle.thedevastator_lombard_language_training_dataset.sk_validation

638.93 KB
10000 rows
4 columns


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

Sl Test

@kaggle.thedevastator_lombard_language_training_dataset.sl_test

496.72 KB
10000 rows
4 columns


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

Sl Train

@kaggle.thedevastator_lombard_language_training_dataset.sl_train

721.09 KB
15000 rows
4 columns


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

Sl Validation

@kaggle.thedevastator_lombard_language_training_dataset.sl_validation

485.62 KB
10000 rows
4 columns


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

So Test

@kaggle.thedevastator_lombard_language_training_dataset.so_test

11.84 KB
100 rows
4 columns


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

So Train

@kaggle.thedevastator_lombard_language_training_dataset.so_train

12.8 KB
100 rows
4 columns


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