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

Oc Validation

@kaggle.thedevastator_lombard_language_training_dataset.oc_validation

11.14 KB
100 rows
4 columns


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

Or Test

@kaggle.thedevastator_lombard_language_training_dataset.or_test

11.6 KB
100 rows
4 columns


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

Or Train

@kaggle.thedevastator_lombard_language_training_dataset.or_train

11.45 KB
100 rows
4 columns


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

Or Validation

@kaggle.thedevastator_lombard_language_training_dataset.or_validation

12.17 KB
100 rows
4 columns


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

Os Test

@kaggle.thedevastator_lombard_language_training_dataset.os_test

12.58 KB
100 rows
4 columns


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

Os Train

@kaggle.thedevastator_lombard_language_training_dataset.os_train

12.04 KB
100 rows
4 columns


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

Os Validation

@kaggle.thedevastator_lombard_language_training_dataset.os_validation

11.81 KB
100 rows
4 columns


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

Pa Test

@kaggle.thedevastator_lombard_language_training_dataset.pa_test

11.63 KB
100 rows
4 columns


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

Pa Train

@kaggle.thedevastator_lombard_language_training_dataset.pa_train

11.66 KB
100 rows
4 columns


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

Pa Validation

@kaggle.thedevastator_lombard_language_training_dataset.pa_validation

11.88 KB
100 rows
4 columns


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

Pdc Test

@kaggle.thedevastator_lombard_language_training_dataset.pdc_test

11.52 KB
100 rows
4 columns


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

Pdc Train

@kaggle.thedevastator_lombard_language_training_dataset.pdc_train

10.56 KB
100 rows
4 columns


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

Pdc Validation

@kaggle.thedevastator_lombard_language_training_dataset.pdc_validation

11.8 KB
100 rows
4 columns


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

Pl Test

@kaggle.thedevastator_lombard_language_training_dataset.pl_test

757.93 KB
10000 rows
4 columns


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

Pl Train

@kaggle.thedevastator_lombard_language_training_dataset.pl_train

1.42 MB
20000 rows
4 columns


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

Pl Validation

@kaggle.thedevastator_lombard_language_training_dataset.pl_validation

754.28 KB
10000 rows
4 columns


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

Pms Test

@kaggle.thedevastator_lombard_language_training_dataset.pms_test

12.46 KB
100 rows
4 columns


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

Pms Train

@kaggle.thedevastator_lombard_language_training_dataset.pms_train

13.86 KB
100 rows
4 columns


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

Pms Validation

@kaggle.thedevastator_lombard_language_training_dataset.pms_validation

13.42 KB
100 rows
4 columns


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

Pnb Test

@kaggle.thedevastator_lombard_language_training_dataset.pnb_test

10.05 KB
100 rows
4 columns


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

Pnb Train

@kaggle.thedevastator_lombard_language_training_dataset.pnb_train

8.8 KB
100 rows
4 columns


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

Pnb Validation

@kaggle.thedevastator_lombard_language_training_dataset.pnb_validation

8.25 KB
100 rows
4 columns


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

Ps Test

@kaggle.thedevastator_lombard_language_training_dataset.ps_test

17.48 KB
100 rows
4 columns


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

Ps Train

@kaggle.thedevastator_lombard_language_training_dataset.ps_train

31.94 KB
100 rows
4 columns


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

Ps Validation

@kaggle.thedevastator_lombard_language_training_dataset.ps_validation

21.67 KB
100 rows
4 columns


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