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

Wa Validation

@kaggle.thedevastator_lombard_language_training_dataset.wa_validation

11.62 KB
100 rows
4 columns


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

Wuu Test

@kaggle.thedevastator_lombard_language_training_dataset.wuu_test

12.06 KB
100 rows
4 columns


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

Wuu Train

@kaggle.thedevastator_lombard_language_training_dataset.wuu_train

14.31 KB
100 rows
4 columns


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

Wuu Validation

@kaggle.thedevastator_lombard_language_training_dataset.wuu_validation

11.85 KB
100 rows
4 columns


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

Xmf Test

@kaggle.thedevastator_lombard_language_training_dataset.xmf_test

14.52 KB
100 rows
4 columns


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

Xmf Train

@kaggle.thedevastator_lombard_language_training_dataset.xmf_train

14.03 KB
100 rows
4 columns


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

Xmf Validation

@kaggle.thedevastator_lombard_language_training_dataset.xmf_validation

16.08 KB
100 rows
4 columns


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

Yi Test

@kaggle.thedevastator_lombard_language_training_dataset.yi_test

11.8 KB
100 rows
4 columns


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

Yi Train

@kaggle.thedevastator_lombard_language_training_dataset.yi_train

12.31 KB
100 rows
4 columns


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

Yi Validation

@kaggle.thedevastator_lombard_language_training_dataset.yi_validation

12.03 KB
100 rows
4 columns


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

Yo Test

@kaggle.thedevastator_lombard_language_training_dataset.yo_test

9.25 KB
100 rows
4 columns


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

Yo Train

@kaggle.thedevastator_lombard_language_training_dataset.yo_train

9.95 KB
100 rows
4 columns


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

Yo Validation

@kaggle.thedevastator_lombard_language_training_dataset.yo_validation

9.64 KB
100 rows
4 columns


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

Zea Test

@kaggle.thedevastator_lombard_language_training_dataset.zea_test

10.88 KB
100 rows
4 columns


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

Zea Train

@kaggle.thedevastator_lombard_language_training_dataset.zea_train

11.42 KB
100 rows
4 columns


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

Zea Validation

@kaggle.thedevastator_lombard_language_training_dataset.zea_validation

11 KB
100 rows
4 columns


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

Zh Classical Test

@kaggle.thedevastator_lombard_language_training_dataset.zh_classical_test

24.76 KB
100 rows
4 columns


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

Zh Classical Train

@kaggle.thedevastator_lombard_language_training_dataset.zh_classical_train

17.74 KB
100 rows
4 columns


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

Zh Classical Validation

@kaggle.thedevastator_lombard_language_training_dataset.zh_classical_validation

20.06 KB
100 rows
4 columns


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

Zh Min Nan Test

@kaggle.thedevastator_lombard_language_training_dataset.zh_min_nan_test

11.62 KB
100 rows
4 columns


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

Zh Min Nan Train

@kaggle.thedevastator_lombard_language_training_dataset.zh_min_nan_train

9.79 KB
100 rows
4 columns


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

Zh Min Nan Validation

@kaggle.thedevastator_lombard_language_training_dataset.zh_min_nan_validation

12.03 KB
100 rows
4 columns


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

Zh Test

@kaggle.thedevastator_lombard_language_training_dataset.zh_test

881.92 KB
10000 rows
4 columns


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

Zh Train

@kaggle.thedevastator_lombard_language_training_dataset.zh_train

1.69 MB
20000 rows
4 columns


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

Zh Validation

@kaggle.thedevastator_lombard_language_training_dataset.zh_validation

906.43 KB
10000 rows
4 columns


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