File size: 1,950 Bytes
a8399a6 dbee375 a8399a6 3a16c35 dbee375 3a16c35 dbee375 3a16c35 dbee375 3a16c35 dbee375 3a16c35 dbee375 3a16c35 9131c3a 3a16c35 |
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 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 |
---
license: apache-2.0
language:
- hu
metrics:
- accuracy
model-index:
- name: huBERTPlain
results:
- task:
type: text-classification
metrics:
- type: f1
value: 0.77
---
## Model description
Cased fine-tuned BERT model for Hungarian, trained on (manuallay anniated) parliamentary pre-agenda speeches scraped from `parlament.hu`.
## Intended uses & limitations
The model can be used as any other (cased) BERT model. It has been tested recognizing emotions at the sentence level in (parliamentary) pre-agenda speeches, where:
* 'Label_0': Neutral
* 'Label_1': Fear
* 'Label_3': Sadness
* 'Label_4': Anger
* 'Label_5': Disgust
* 'Label_6': Success
* 'Label_7': Joy
## Training
Fine-tuned version of the original huBERT model (`SZTAKI-HLT/hubert-base-cc`), trained on HunEmPoli corpus.
## Eval results
| Class | Precision | Recall | F-Score |
|-----|------------|------------|------|
| Fear | 0.625 | 0.625 | 0.625 |
| Sadness | 0.8535 | 0.6291 | 0.7243 |
| Anger | 0.7857 | 0.3437 | 0.4782 |
| Disgust | 0.7154 | 0.8790 | 0.7888 |
| Success | 0.8579 | 0.8683 | 0.8631 |
| Joy | 0.549 | 0.6363 | 0.5894 |
| Trust | 0.4705 | 0.5581 | 0.5106 |
| Macro AVG | 0.7134 | 0.6281 | 0.6497 |
| Weighted AVG | 0.791 | 0.7791 | 0.7743 |
## Usage
```py
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("poltextlab/HunEmBERT8")
model = AutoModelForSequenceClassification.from_pretrained("poltextlab/HunEmBERT8")
```
### BibTeX entry and citation info
If you use the model, please cite the following paper:
Bibtex:
```bibtex
@ARTICLE{10149341,
author={{"U}veges, Istv{\'a}n and Ring, Orsolya},
journal={IEEE Access},
title={HunEmBERT: a fine-tuned BERT-model for classifying sentiment and emotion in political communication},
year={2023},
volume={},
number={},
pages={1-1},
doi={10.1109/ACCESS.2023.3285536}
}
``` |