---
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](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

```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('NYTK/sentence-transformers-experimental-hubert-hungarian')
embeddings = model.encode(sentences)
print(embeddings)
```

## 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}
}
```