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

Lmo Validation

@kaggle.thedevastator_lombard_language_training_dataset.lmo_validation

12.05 KB
100 rows
4 columns


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

Ln Test

@kaggle.thedevastator_lombard_language_training_dataset.ln_test

12.88 KB
100 rows
4 columns


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

Ln Train

@kaggle.thedevastator_lombard_language_training_dataset.ln_train

11.36 KB
100 rows
4 columns


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

Ln Validation

@kaggle.thedevastator_lombard_language_training_dataset.ln_validation

10.74 KB
100 rows
4 columns


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

Lt Test

@kaggle.thedevastator_lombard_language_training_dataset.lt_test

608.6 KB
10000 rows
4 columns


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

Lt Train

@kaggle.thedevastator_lombard_language_training_dataset.lt_train

614.53 KB
10000 rows
4 columns


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

Lt Validation

@kaggle.thedevastator_lombard_language_training_dataset.lt_validation

610.89 KB
10000 rows
4 columns


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

Lv Test

@kaggle.thedevastator_lombard_language_training_dataset.lv_test

553.06 KB
10000 rows
4 columns


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

Lv Train

@kaggle.thedevastator_lombard_language_training_dataset.lv_train

562.73 KB
10000 rows
4 columns


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

Lv Validation

@kaggle.thedevastator_lombard_language_training_dataset.lv_validation

551.65 KB
10000 rows
4 columns


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

Map Bms Test

@kaggle.thedevastator_lombard_language_training_dataset.map_bms_test

11.3 KB
100 rows
4 columns


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

Map Bms Train

@kaggle.thedevastator_lombard_language_training_dataset.map_bms_train

12.1 KB
100 rows
4 columns


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

Map Bms Validation

@kaggle.thedevastator_lombard_language_training_dataset.map_bms_validation

10.1 KB
100 rows
4 columns


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

Mg Test

@kaggle.thedevastator_lombard_language_training_dataset.mg_test

9.64 KB
100 rows
4 columns


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

Mg Train

@kaggle.thedevastator_lombard_language_training_dataset.mg_train

9.71 KB
100 rows
4 columns


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

Mg Validation

@kaggle.thedevastator_lombard_language_training_dataset.mg_validation

9.62 KB
100 rows
4 columns


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

Mhr Test

@kaggle.thedevastator_lombard_language_training_dataset.mhr_test

11.87 KB
100 rows
4 columns


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

Mhr Train

@kaggle.thedevastator_lombard_language_training_dataset.mhr_train

10.45 KB
100 rows
4 columns


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

Mhr Validation

@kaggle.thedevastator_lombard_language_training_dataset.mhr_validation

11.82 KB
100 rows
4 columns


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

Min Test

@kaggle.thedevastator_lombard_language_training_dataset.min_test

11.14 KB
100 rows
4 columns


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

Min Train

@kaggle.thedevastator_lombard_language_training_dataset.min_train

12.06 KB
100 rows
4 columns


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

Min Validation

@kaggle.thedevastator_lombard_language_training_dataset.min_validation

12.83 KB
100 rows
4 columns


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

Mi Test

@kaggle.thedevastator_lombard_language_training_dataset.mi_test

8.45 KB
100 rows
4 columns


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

Mi Train

@kaggle.thedevastator_lombard_language_training_dataset.mi_train

9.97 KB
100 rows
4 columns


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

Mi Validation

@kaggle.thedevastator_lombard_language_training_dataset.mi_validation

9.09 KB
100 rows
4 columns


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