Instructions to use EMBEDDIA/finest-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EMBEDDIA/finest-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="EMBEDDIA/finest-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("EMBEDDIA/finest-bert") model = AutoModelForMaskedLM.from_pretrained("EMBEDDIA/finest-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- fi
- et
- en
- multilingual
license: cc-by-4.0
FinEst BERT
FinEst BERT is a trilingual model, using bert-base architecture, trained on Finnish, Estonian, and English corpora. Focusing on three languages, the model performs better than multilingual BERT, while still offering an option for cross-lingual knowledge transfer, which a monolingual model wouldn't.
Evaluation is presented in our article:
@Inproceedings{ulcar-robnik2020finest,
author = "Ulčar, M. and Robnik-Šikonja, M.",
year = 2020,
title = "{FinEst BERT} and {CroSloEngual BERT}: less is more in multilingual models",
editor = "Sojka, P and Kopeček, I and Pala, K and Horák, A",
booktitle = "Text, Speech, and Dialogue {TSD 2020}",
series = "Lecture Notes in Computer Science",
volume = 12284,
publisher = "Springer",
url = "https://doi.org/10.1007/978-3-030-58323-1_11",
}
The preprint is available at arxiv.org/abs/2006.07890.