Instructions to use NYTK/sentiment-hts5-hubert-hungarian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NYTK/sentiment-hts5-hubert-hungarian with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NYTK/sentiment-hts5-hubert-hungarian")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NYTK/sentiment-hts5-hubert-hungarian") model = AutoModelForSequenceClassification.from_pretrained("NYTK/sentiment-hts5-hubert-hungarian") - Notebooks
- Google Colab
- Kaggle
Hungarian Sentence-level Sentiment Analysis with Finetuned huBERT Model
For further models, scripts and details, see our repository or our demo site.
- Pretrained model used: huBERT
- Finetuned on Hungarian Twitter Sentiment (HTS) Corpus
- Labels: 0 (very negative), 1 (negative), 2 (neutral), 3 (positive), 4 (very 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:
@article {laki-yang-sentiment,
author = {Laki, László János and Yang, Zijian Győző},
title = {Sentiment Analysis with Neural Models for Hungarian},
journal = {Acta Polytechnica Hungarica},
year = {2023},
publisher = {Obuda University},
volume = {20},
number = {5},
doi = {10.12700/APH.20.5.2023.5.8},
pages= {109--128},
url = {https://acta.uni-obuda.hu/Laki_Yang_134.pdf}
}
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