The model can be used with Transformers pipeline for NER. Try it in Google Colab, where the Transformers library is pre-installed or on your local machine (preferably using a virtual environment, see tutorial below) and install the Transformers library using pip install transformers.

from transformers import pipeline

ner = pipeline("ner", model="vbius01/est-roberta-ud-ner")

text = "Eesti kuulub erinevalt Lätist ja Leedust kahtlemata Põhjamaade kultuuriruumi."
results = ner(text)

print(results)

[{'entity': 'B-GEP', 'score': np.float32(0.99339926), 'index': 1, 'word': '▁Eesti', 'start': 0, 'end': 5}, {'entity': 'B-GEP', 'score': np.float32(0.9923631), 'index': 4, 'word': '▁Lätist', 'start': 22, 'end': 29}, {'entity': 'B-GEP', 'score': np.float32(0.990756), 'index': 6, 'word': '▁Leedust', 'start': 32, 'end': 40}, {'entity': 'B-LOC', 'score': np.float32(0.61792), 'index': 8, 'word': '▁Põhjamaade', 'start': 51, 'end': 62}]

Repository: github.com/martinkivisikk/ner_thesis
Paper: Developing a NER Model Based on Treebank Corpora

Virtual environment setup

Create and activate a virtual environment in your project directory with venv.

python -m venv .env
source .env/bin/activate

Uses

This model can be used to find named entities from Estonian texts.