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Datasets:
Azerbaijani Named Entity Recognition (NER) Dataset
This repository contains the dataset for training and evaluating Named Entity Recognition (NER) models in the Azerbaijani language. The dataset includes annotated text data with various named entities.
Dataset Description
The dataset includes the following entity types:
- 0: O: Outside any named entity
- 1: PERSON: Names of individuals
- 2: LOCATION: Geographical locations, both man-made and natural
- 3: ORGANISATION: Names of companies, institutions
- 4: DATE: Dates or periods
- 5: TIME: Times of the day
- 6: MONEY: Monetary values
- 7: PERCENTAGE: Percentage values
- 8: FACILITY: Buildings, airports, etc.
- 9: PRODUCT: Products and goods
- 10: EVENT: Events and occurrences
- 11: ART: Artworks, titles of books, songs
- 12: LAW: Legal documents
- 13: LANGUAGE: Languages
- 14: GPE: Countries, cities, states
- 15: NORP: Nationalities or religious or political groups
- 16: ORDINAL: Ordinal numbers
- 17: CARDINAL: Cardinal numbers
- 18: DISEASE: Diseases and medical conditions
- 19: CONTACT: Contact information, e.g., phone numbers, emails
- 20: ADAGE: Proverbs, sayings
- 21: QUANTITY: Measurements and quantities
- 22: MISCELLANEOUS: Miscellaneous entities
- 23: POSITION: Professional or social positions
- 24: PROJECT: Names of projects or programs
Data Format
The dataset is in CSV format with the following columns:
index
: Unique identifier for each row.tokens
: A list of tokens in the text.ner_tags
: A list of corresponding NER tags for each token.
License
This model licensed under the CC BY-NC-ND 4.0 license. What does this license allow?
Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Non-Commercial: You may not use the material for commercial purposes.
No Derivatives: If you remix, transform, or build upon the material, you may not distribute the modified material.
For more information, please refer to the CC BY-NC-ND 4.0 license.
Contact
For more information, questions, or issues, please contact LocalDoc at [v.resad.89@gmail.com].
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Token Classification
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Updated