license: apache-2.0
language:
- hu
library_name: sentence-transformers
tags:
- sentence-similarity
widget:
- source_sentence: Szép napunk van.
sentences:
- Jó az idő.
- Szép az autó.
- Elutazok egy napra.
example_title: Példa
Hungarian Experimental Sentence-BERT
The pre-trained hubert-base-cc[https://huggingface.co/SZTAKI-HLT/hubert-base-cc] was fine-tuned on the Hunglish 2.0[http://mokk.bme.hu/resources/hunglishcorpus/] parallel corpus to mimic the bert-base-nli-stsb-mean-tokens[https://huggingface.co/sentence-transformers/bert-base-nli-stsb-mean-tokens] model provided by UKPLab. Sentence embeddings were obtained by applying mean pooling to the huBERT output. The data was split into training (98%) and validation (2%) sets. By the end of the training, a mean squared error of 0.106 was computed on the validation set. Our code was based on the Sentence-Transformers[https://www.sbert.net] library. Our model was trained for 2 epochs on a single GTX 1080Ti GPU card with the batch size set to 32. The training took approximately 15 hours.
Limitations
- max_seq_length = 128
Usage
Citation
If you use this model, please cite the following paper:
@article {bertopic,
title = {Analyzing Narratives of Patient Experiences: A BERT Topic Modeling Approach},
journal = {Acta Polytechnica Hungarica},
year = {2023},
author = {Osváth, Mátyás and Yang, Zijian Győző and Kósa, Karolina},
pages = {153--171},
volume = {20},
number = {7}
}