Uk Ner Web Trf 13class

🚀 uk_ner_web_trf_13class

uk_ner_web_trf_13class is a fine - tuned model designed for Named Entity Recognition. It is based on the Roberta Large Ukrainian model and achieves state - of - the - art performance in the NER task for the Ukrainian language.

✨ Features

• Entity Recognition: Capable of recognizing thirteen types of entities, including ORG (organizations), PERS (persons), LOC (locations), and more.
• Fine - Tuned: Fine - tuned on the NER - UK 2.0 dataset released by lang - uk.

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

No code examples are provided in the original document, so this section is skipped.

📚 Documentation

Model Description

uk_ner_web_trf_13class is a fine - tuned Roberta Large Ukrainian model ready for Named Entity Recognition. It sets a new SoA performance for the NER task in the Ukrainian language.

It has strong performance and is trained to recognize thirteen types of entities:

• ORG — A name of a company, brand, agency, organization, institution (including religious, informal, non - profit), party, people's association, or specific project like a conference, a music band, a TV program, etc. Example: UNESCO.
• PERS — A person name where person may refer to humans, book characters, or humanoid creatures like vampires, ghosts, mermaids, etc. Example: Marquis de Sade.
• LOC — A geographical name, including names of districts, villages, cities, states, counties, countries, continents, rivers, lakes, seas, oceans, mountains, etc. Example: Ukraine.
• MON — A sum of money including the currency. Examples: $40, 1 mln hryvnias.
• PCT — A percent value including the percent sign or the word "percent". Example: 10%.
• DATE — A full or incomplete calendar date that may include a century, a year, a month, a day. Examples: last week, 10.12.1999.
• TIME — A textual or numerical timestamp. Examples: half past six, 18:30.
• PERIOD — A time period, which may consist of two dates. Examples: a few months, 2014 - 2015.
• JOB — A job title. Examples: member of parliament, ophthalmologist.
• DOC — A unique name of a document, including names of contracts, orders, bills, purchases. Example: procurement contract CW2244226.
• QUANT — A quantity with the unit of measurement, such as weight, distance, size. Examples: 3 kilograms, a hundred miles.
• ART (artifact) — A name of a human - made product, like a book, a song, a car, or a sandwich. Examples: Mona Lisa, iPhone.
• MISC — Any other entity not covered in the list above, like names of holidays, websites, battles, wars, sports events, hurricanes, etc. Example: Black Friday.

The model was fine - tuned on the NER - UK 2.0 dataset, released by the lang - uk.

Another transformer - based model trained on 4 classes for the SpaCy is available here.

Model Metrics

🔧 Technical Details

No specific technical implementation details (more than 50 words) are provided in the original document, so this section is skipped.

📄 License

This project is licensed under the MIT license.