PaReS-sentimenTw-political-PL
This model is a fine-tuned version of dkleczek/bert-base-polish-cased-v1 to predict 3-categorical sentiment. Fine-tuned on 1k sample of manually annotated Twitter data.
Model developed as a part of ComPathos project: https://www.ncn.gov.pl/sites/default/files/listy-rankingowe/2020-09-30apsv2/streszczenia/497124-en.pdf
from transformers import pipeline
model_path = "eevvgg/PaReS-sentimenTw-political-PL"
sentiment_task = pipeline(task = "sentiment-analysis", model = model_path, tokenizer = model_path)
sequence = ["Cała ta śmieszna debata była próbą ukrycia problemów gospodarczych jakie są i nadejdą, pytania w większości o mało istotnych sprawach",
"Brawo panie ministrze!"]
result = sentiment_task(sequence)
labels = [i['label'] for i in result] # ['Negative', 'Positive']
Model Sources
- BibTex citation:
@misc{SentimenTwPLGK2023,
author={Gajewska, Ewelina and Konat, Barbara},
title={PaReSTw: BERT for Sentiment Detection in Polish Language},
year={2023},
howpublished = {\url{https://huggingface.co/eevvgg/PaReS-sentimenTw-political-PL}},
}
Intended uses & limitations
Sentiment detection in Polish data (fine-tuned on tweets from political domain).
Training and evaluation data
- Trained for 3 epochs, mini-batch size of 8.
- Training results: loss: 0.1358926964368792
It achieves the following results on the test set (10%):
-
No. examples = 100
-
mini batch size = 8
-
accuracy = 0.950
-
macro f1 = 0.944
precision recall f1-score support 0 0.960 0.980 0.970 49 1 0.958 0.885 0.920 26 2 0.923 0.960 0.941 25