File size: 1,186 Bytes
8c63dfa e4f7afe 8c63dfa e4f7afe 8c63dfa e4f7afe 8c63dfa a097122 8c63dfa 9e78df9 bf73dde fa1fc71 8c63dfa f100823 8c63dfa b17bf83 8c63dfa a50727a 8c63dfa 4fecb6c 8c63dfa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 |
---
language:
- hu
tags:
- text-classification
license: mit
metrics:
- accuracy
widget:
- text: Jó reggelt! majd küldöm az élményhozókat :).
---
# Hungarian Sentence-level Sentiment Analysis Model with XLM-RoBERTa
For further models, scripts and details, see [our repository](https://github.com/nytud/sentiment-analysis) or [our demo site](https://juniper.nytud.hu/demo/nlp).
- Pretrained model used: XLM-RoBERTa base
- Finetuned on Hungarian Twitter Sentiment (HTS) Corpus
- Labels: 0 (negative), 1 (positive)
## Limitations
- max_seq_length = 128
## Results
| Model | HTS2 | HTS5 |
| ------------- | ------------- | ------------- |
| huBERT | 85.56 | 68.99 |
| XLM-RoBERTa| **85.56** | 66.50 |
## Citation
If you use this model, please cite the following paper:
```
@inproceedings {laki-yang-sentiment,
title = {Improving Performance of Sentence-level Sentiment Analysis with Data Augmentation Methods},
booktitle = {Proceedings of 12th IEEE International Conference on Cognitive Infocommunications (CogInfoCom 2021)},
year = {2021},
publisher = {IEEE},
address = {Online},
author = {Laki, László and Yang, Zijian Győző}
pages = {417--422}
}
``` |